Curso Introduction to AI and Machine Learning on Google Cloud

Este curso está dirigido a profesionales en roles relacionados con las finanzas o TI, responsables de optimizar los costos en la nube de sus organizaciones. Es ideal para quienes desean comprender conceptos y prácticas de FinOps, incluyendo la gobernanza financiera y la gestión de costos en Google Cloud.

• Abordar los desafíos de gobernanza financiera con Cloud FinOps.
• Comprender los cinco pilares del modelo operativo de Google Cloud FinOps.
• Establecer políticas y habilitación de Cloud FinOps para tu organización.
• Impulsar la responsabilidad financiera y la realización de valor con métricas de éxito definidas.
• Optimizar los costos de los recursos utilizados en Google Cloud.
• Modernizar métodos de presupuesto y previsión.
• Utilizar herramientas de costos para gestionar y supervisar eficazmente el gasto en Google Cloud.

Módulo 1: Introducción

Objetivos:

• Describir los desafíos de gobernanza financiera para recursos en la nube.
• Analizar las tendencias de la industria en FinOps.
• Comprender el marco de trabajo FinOps de Google Cloud.

Contenidos:

• Desafíos de gobernanza financiera
• Tendencias de la industria en FinOps
• Marco de trabajo FinOps de Google Cloud

Módulo 2: Responsabilidad y Habilitación

Objetivos:

• Crear un modelo operativo de Cloud FinOps.
• Definir roles y responsabilidades dentro de un equipo de Cloud FinOps.
• Establecer gobernanza y políticas.
• Habilitar la organización mediante incentivos.

Contenidos:

• Modelo operativo de Cloud FinOps
• Roles y responsabilidades
• Gobernanza y políticas
• Incentivación
• Actividad: Establecer un modelo operativo de Cloud FinOps

Módulo 3: Medición y Realización

Objetivos:

• Establecer KPIs y métricas de valor.
• Comprender la relación valor vs. costo.
• Mejorar la transparencia a través de informes y responsabilidad.

Contenidos:

• Beneficios de medición y realización
• Indicadores clave de rendimiento (KPIs) y métricas de valor
• Relación valor vs. costo
• Transparencia mediante informes y responsabilidad
• Actividades:
  • Relación valor vs. costo
  • Transparencia mediante informes y responsabilidad

Módulo 4: Optimización de Costos

Objetivos:

• Comprender el enfoque de optimización de costos en Google Cloud.
• Implementar visibilidad en la utilización de recursos mediante etiquetas.
• Comprender las señales clave (golden signals).
• Describir estrategias de optimización para recursos, precios y arquitectura.

Contenidos:

• Enfoque de optimización de costos en Google Cloud
• Utilización de recursos
• Señales clave (golden signals)
• Optimización de recursos, precios y arquitectura
• Actividades:
  • Comprender las señales clave
  • Optimización de recursos, precios y arquitectura

Módulo 5: Planificación y Pronósticos

Objetivos:

• Comprender los procesos de presupuestación y asignación.
• Pronosticar los gastos en recursos de la nube.
• Implementar procesos de informes e integración.
• Entender el modelo de facturación de Google Cloud.

Contenidos:

• Presupuestación y asignación
• Pronóstico de gastos
• Informes e integración
• Modelo de facturación de Google Cloud
• Actividad: Presupuestación, asignación y pronóstico

Módulo 6: Herramientas y Aceleradores

Objetivos:

• Explorar presupuestos y alertas en Google Cloud.
• Utilizar herramientas de automatización de etiquetas y gestión de recursos.
• Describir las herramientas de gestión de costos en Google Cloud.
• Comprender los informes de costos en la nube.

Contenidos:

• Presupuestos y alertas en Google Cloud
• Automatización de etiquetas y gestión de recursos
• Herramientas de gestión de costos en Google Cloud
• Informes de costos en la nube

Documentación oficial para el curso Getting Started with FinOps on 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.

Este curso está dirigido a arquitectos de nube, ingenieros DevOps, analistas de costes en la nube y otros profesionales técnicos que buscan buscan implementar prácticas avanzadas de FinOps en sus organizaciones.

• Comprender los principios básicos de FinOps y cómo conecta la tecnología con las finanzas en la nube.
• Explorar técnicas comprobadas para identificar ineficiencias de costos, optimizar el uso de recursos y lograr ahorros significativos.
• Utilizar herramientas avanzadas de monitoreo e información basada en datos para rastrear patrones de gasto, prever costos y tomar decisiones informadas.
• Implementar modelos efectivos de asignación de costos para atribuir de manera precisa los gastos en la nube a equipos o proyectos específicos.
• Aprovechar IaC (Infraestructura como Código) para automatizar procesos de gestión de costos, aplicar políticas de gasto y garantizar una gobernanza financiera coherente.
• Utilizar plataformas y herramientas líderes en la industria FinOps para obtener una visibilidad completa de los gastos en la nube y optimizar el uso de recursos.
• Aplicar los conocimientos adquiridos en escenarios del mundo real a través de ejercicios prácticos y estudios de caso.
• Fomentar una cultura de conciencia y responsabilidad sobre los costos dentro de tu organización.

Módulo 1: Fundamentos de FinOps en Google Cloud

Objetivos:

• Reconocer los desafíos actuales en la gobernanza financiera.
• Comprender el propósito del marco de FinOps en Google Cloud.
• Implementar el modelo operativo de FinOps.
• Identificar las fases y pilares del viaje FinOps.

Contenidos:

• Retos en la gobernanza financiera
• Marco de FinOps de Google Cloud
• Modelo operativo de FinOps en la nube
• Viaje de FinOps en Google

Módulo 2: Exploración y comprensión de los datos de facturación

Objetivos:

• Gestionar cuentas de facturación de Google Cloud.
• Navegar por el panel de facturación.
• Controlar el acceso a las cuentas de facturación mediante IAM.
• Configurar y utilizar BigQuery para exportar datos de facturación.
• Visualizar los costos con Looker Studio.
• Laboratorio: Exploración del panel de facturación.
• Laboratorio: Visualización de costos con Looker Studio.

Contenidos:

• Cuentas de facturación en Google Cloud
• Panel de control de facturación en la nube
• Acceso a cuentas de facturación
• Exportación de datos de facturación a BigQuery
• Conexión de Looker Studio a los datos de facturación

Módulo 3: Etiquetado y etiquetado de recursos

Objetivos:

• Utilizar etiquetas en los recursos.
• Definir y asignar etiquetas a proyectos y recursos.
• Acceder a la información de etiquetas en los datos de facturación.

Contenidos:

• Etiquetas
• Etiquetado de recursos en los datos de facturación
• Laboratorio: Etiquetado de recursos.
• Laboratorio: Análisis de etiquetas y datos de facturación en BigQuery.

Módulo 4: Presupuestos y alertas

Objetivos:

• Comprender los beneficios de los presupuestos y alertas.
• Crear un presupuesto.
• Configurar presupuestos utilizando gcloud, Python y Terraform.

Contenidos:

• Presupuestos y alertas
• Roles y permisos de presupuestos
• Automatización de la creación de presupuestos
• Demostración: Uso de gcloud y Terraform para crear presupuestos y alertas.

Módulo 5: Gestión y optimización de costos en BigQuery

Objetivos:

• Comprender los costos de cómputo y almacenamiento en BigQuery.
• Utilizar herramientas y consultas para gestionar y analizar los costos de BigQuery.
• Identificar prácticas recomendadas para la gestión de costos en BigQuery.

Contenidos:

• Costos de consultas
• Costos de almacenamiento
• Asignación de costos de BigQuery
• Otras consideraciones sobre costos de BigQuery
• Laboratorio: Análisis de costos de BigQuery a nivel de proyecto, carpeta y organización.

Módulo 6: Gestión y optimización de costos en Compute Engine y GKE

Objetivos:

• Usar eficientemente los recursos en la nube para evitar desperdicios.
• Comprender los costos asociados a cómputo y red en Google Cloud.
• Identificar costos adicionales relacionados con Google Kubernetes Engine.

Contenidos:

• Gestión de costos y reducción de desperdicios en cómputo
• Gestión de costos en Compute Engine
• Comprensión de costos de red
• Gestión de costos en Google Kubernetes Engine (GKE)
• Laboratorio: Revisión y comprensión de costos de cómputo.
• Laboratorio: Revisión y comprensión de costos en Kubernetes.

Módulo 7: Gestión de costos de almacenamiento y bases de datos

Objetivos:

• Optimizar costos de almacenamiento en la nube considerando ubicación, patrones de acceso y ciclo de vida.
• Comprender costos de bases de datos SQL, como Cloud SQL, AlloyDB y Spanner.
• Utilizar descuentos por compromisos de uso en bases de datos.
• Comprender costos de bases de datos NoSQL, como Memorystore, Firestore y Bigtable.

Temas:

• Almacenamiento en la nube
• Bases de datos SQL en Google Cloud
• Bases de datos NoSQL en Google Cloud
• Laboratorio: Gestión de costos en Cloud Storage.
• Laboratorio: Análisis de facturación de bases de datos.

Módulo 8: Gestión de costos en Machine Learning y Vertex AI

Objetivos:

• Identificar los costos asociados al entrenamiento y despliegue de modelos de machine learning.
• Comprender los costos de suscripción y uso de API para IA generativa.

Coontenidos:

• Entrenamiento y hosting de modelos de machine learning
• Monitoreo de costos en Vertex AI
• Costos relacionados con IA generativa
• Laboratorio: Análisis de costos de Vertex AI en los datos de facturación.

Módulo 9: Automatización de actividades de facturación

Objetivos:

• Implementar técnicas para automatizar la gestión de recursos.
• Responder programáticamente a mensajes de alertas de presupuesto en Pub/Sub.
• Utilizar el servicio de inventario de activos en la nube.
• Visualizar recomendaciones en el Google FinOps Hub.

Contenidos:

• Automatización de gestión de recursos
• Servicio de inventario de activos en la nube
• Acciones programáticas basadas en alertas de presupuesto
• Google FinOps Hub
• Laboratorio: Automatización del etiquetado al crear recursos utilizando Asset API, Pub/Sub y Cloud Run.
• Laboratorio: Programación de acciones de alertas de presupuesto con Pub/Sub y Cloud Run.

Módulo 10: Construcción de una práctica de FinOps

Objetivos:

• Establecer un equipo y hoja de ruta para FinOps.
• Implementar procesos y flujos de trabajo efectivos para FinOps.
• Definir criterios para evaluar el éxito de una estrategia de FinOps.
• Reconocer la importancia de la mejora continua y la evolución de FinOps.

Contenidos:

• Equipo de FinOps
• Metas, hitos y métricas
• Procesos y flujos de trabajo
• Evaluación del éxito
• Mejora continua
• Laboratorio: Desafío final de FinOps.

Documentación oficial para el curso Technical Foundations of FinOps on 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.

Cualquier profesional comercial o equipo de una organización interesado en aprender sobre los cambios operativos con la tecnología de Google Cloud, incluyendo la administración de costos, la seguridad y el monitoreo de recursos de TI.

• Describir la gobernanza financiera en la nube y las mejores prácticas recomendadas de Google Cloud para una gestión eficaz de los costos de la nube.
• Definir un modelo de responsabilidad compartida para una seguridad óptima en la nube.
• Explicar cómo los equipos de TI y los líderes empresariales pueden usar los principios de DevOps y SRE para aumentar la eficiencia operativa de la nube.
• Identificar las soluciones de Google Cloud para el monitoreo de recursos en la nube y la gestión del rendimiento de las aplicaciones.

Módulo 1: Introduction

Objetivos:

Gain an overview of the course structure and content breakdown.

Temas:

• Course introduction
• Student slides

Módulo 2: Financial Governance in the Cloud

Objetivos:

• Explain how to think about the total cost of ownership (TCO) in the cloud.
• Describe best practices for effective cloud cost management.

Temas:

• Introduction
• Fundamentals of cloud cost management
• Total cost of ownership
• Best practices for managing Google Cloud costs

Módulo 3: Security in the cloud

Objetivos:

• Identify today’s top cybersecurity challenges and threats to data privacy.
• Explain what is meant by a ‘Shared Responsibility Model’ and how customers can use Google Cloud tools to implement their chosen security policies.

Temas:

• Introduction
• Fundamental terms: Privacy, security, compliance and availability
• Today’s top cyber security challenges
• Shared Responsibility Model – Part I
• Shared Responsibility Model – Part II
• Identity and access management
• Resource hierarchy

Módulo 4: Monitoring Cloud IT Services and Operations

Objetivos:

• Define monitoring, logging, and observability within the context of cloud operations.
• Identify the Google Cloud resource monitoring and maintenance tools.
• Differentiate DevOps from SRE.

Temas:

• Introduction
• IT development and operations challenges
• DevOps and SRE
• Google Cloud resource monitoring tools

Módulo 5: Summary

Objetivos:

Breakdown of module learning objectives

Temas:

• Review the key topics and core concepts covered in this course.
• Find additional resources to continue your learning.

Documentación oficial para el curso Understanding Google Cloud Security and Operations.

• 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.

Cualquier profesional de negocios o equipo de una organización interesado en aprender a desbloquear el valor de los sistemas de TI tradicionales y modernizar las aplicaciones con la tecnología de Google Cloud puede beneficiarse de este curso.

• Describir los beneficios clave de la modernización de la infraestructura y las aplicaciones con referencia a la tecnología de la nube.
• Diferenciar entre máquinas virtuales, contenedores y Kubernetes.
• Explicar las soluciones de Google Cloud que ayudan a las empresas a administrar mejor sus sistemas, como App Engine y Google Kubernetes Engine.
• Explicar cómo las soluciones de Google Cloud respaldan la modernización de aplicaciones y simplifican la administración de API.

Módulo 1: Introduction

Objetivos:

Gain an overview of the course structure and content breakdown.

Temas:

• Course introduction
• Student slides

Módulo 2: Modernizing IT Infrastructure with Google Cloud

Objetivos:

• Explain why legacy infrastructure struggles to deliver modern services to customers.
• Define private, hybrid, and multi-cloud infrastructures, and how businesses gain value from them.
• Identify the Google Cloud solutions that help businesses modernize their infrastructure.

Temas:

• Introduction
• Infrastructure Modernization
• Understanding compute options in the cloud
• Hybrid and multi-cloud architectures
• Google Cloud Compute Solutions

Módulo 3: Modernizing Applications with Google Cloud

Objetivos:

• Describe patterns for modernizing and developing applications.
• Explain Google Cloud core solutions for application development and modernization.

Temas:

• Introduction
• Cloud change patterns
• Challenges in application development
• Google Kubernetes Engine
• App Engine
• Customer case studies

Módulo 4: The Value of APIs

Objetivos:

• Explain how APIs can create new business models and support app modernization.
• Explain how Apigee simplifies API management.

Temas:

• Introduction
• Legacy system challenges
• How APIs can modernize legacy systems
• Using APIs to create new business value
• Apigee

Módulo 5: Summary

Objetivos:

Breakdown of module learning objectives

Temas:

• Review the key topics and core concepts covered in this course.
• Find additional resources to continue your learning.

Documentación oficial para el curso Infrastructure and Application Modernization 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.

Cualquier profesional de negocios o equipo de una organización interesado en aprender sobre la nube, los datos y la tecnología de Google Cloud.

• Describir el papel de los datos en la transformación digital y la importancia de una cultura basada en datos.
• Identificar soluciones comunes de Google Cloud para la gestión de datos.
• Identificar soluciones comunes de Google Cloud para análisis inteligente.
• Identificar las soluciones de Google Cloud para Machine Learning e inteligencia artificial.

Módulo 1: Introduction

Objetivos:

Gain an overview of the course structure and content breakdown.

Temas:

Course introduction

Módulo 2: The Value of Data

Objetivos:

• Describe the role of data in digital transformation.
• Describe how to use data to create business insights.
• Recognize examples of structured and unstructured data.

Temas:

• Introduction
• The role of data in digital transformation
• Leveraging data in your organization
• Understanding data types
• Important data considerations

Módulo 3: Data Consolidation and Analytics

Objetivos:

• Explain the benefits of storing data in the cloud.
• Define key terms such as database, data warehouse, and data lake.
• Identify Google Cloud data storage and database services.
• Identify key Google Cloud products that use data to create business insights, including Looker and Big Query.

Temas:

• Introduction
• Migrating your data to the cloud
• Cloud databases
• Cloud data warehouses
• Cloud data lakes
• Business intelligence solutions (e.g. BigQuery, Looker)

Módulo 4: Innovation with Machine Learning

Objetivos:

• Define artificial intelligence (AI) and machine learning (ML).
• Explain the importance of data quality in ML prediction accuracy.
• Describe Google Cloud’s differentiators when it comes to AI and Machine Learning.
• Recognize ways customers have used Google Cloud’s AI and ML products to innovate.

Temas:

• Introduction
• What is machine learning?
• Data Quality
• AI and ML with Google Cloud
• Real-world use cases for ML

Módulo 5: Summary

Objetivos:

• Review the key topics and core concepts covered in this course.
• Find additional resources to continue your learning.

Temas:

Course summary

Documentación oficial para el curso Innovating with Data and 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.

Cualquier profesional de negocios o equipo de una organización interesado en aprender sobre la nube, los datos y la transformación digital con la tecnología de Google Cloud.

• Definir conceptos fundamentales como datos, tecnología en la nube y transformación digital.
• Utilizar casos de uso de clientes para demostrar cómo la tecnología de la nube está transformando las empresas.
• Reconocer los pilares de la solución de Google Cloud y los casos de uso de cada uno.
• Describir los principios clave que ayudan a escalar una mentalidad de innovación en toda una organización.

Módulo 1: Introduction

Objetivos:

Gain an overview of the course structure and content breakdown.

Temas:

• Course introduction
• Student slides

Módulo 2: Why cloud technology is revolutionizing business

Objetivos:

• Explain why and how the cloud revolutionizes businesses.
• Define key terms such as cloud, cloud technology, data, and digital transformation.

Temas:

• Introduction
• What is cloud?
• Danger of maintaining the status quo
• A paradigm shift
• Extraordinary compute power
• Data as currency

Módulo 3: Digital Transformation with Google Cloud

Objetivos:

• Recognize common challenges and opportunities that businesses might encounter in their digital transformation journey.
• Identify Google Cloud solution pillars and common use cases for each.
• Recognize the Google Cloud Adoption Framework (GCAF).

Temas:

• Introduction
• Enhancing productivity and collaboration
• Modernizing IT infrastructure
• Modernizing business platforms and applications
• Unlocking the value of data
• Using a new built-in security model
• Google Cloud solutions for digital transformation
• Google Cloud Adoption Framework (GCAF)

Módulo 4: Scaling the innovation mindset

Objetivos:

• List Google’s 6 focus areas for organizational transformation.
• Define innovation and principles for scaling the innovation mindset.
• Explain the importance of focusing on your users and use Google’s methods to identify user expectations.
• Familiarize yourself with how a business case for the cloud is created.

Temas:

• Introduction
• Focus areas for culture transformation
• Scale the innovation mindset
• Think 10X
• Launch and iterate
• Innovation example: Banking industry
• Try it yourself

Módulo 5: Summary

Objetivos:

Breakdown of module learning objectives

Temas:

• Review the key topics and core concepts covered in this course.
• Find additional resources to continue your learning.

Documentación oficial para el curso Introduction to Digital Transformation 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.

Administradores de Google Workspace nuevos o existentes.

• Aprender a crear y configurar una nueva cuenta de Google Workspace con un dominio principal y explorar opciones para el aprovisionamiento.
• Aprender a administrar tus usuarios y familiarizarte con las estructuras organizativas y los servicios principales de Google Workspace, como Gmail y Calendar. Aprender a configurar estos servicios para satisfacer las necesidades de tu organización.
• Conocer las prácticas recomendadas de Google para proteger a tus usuarios y datos. Esto incluye examinar la seguridad de los usuarios y las aplicaciones y familiarizarte con las opciones de inicio de sesión único (SSO) disponibles para tu organización. Aprender a identificar eventos y riesgos de seguridad y mitigar los problemas que puedan surgir.
• Aprender a configurar el cumplimiento del correo electrónico e implementar medidas para proteger tu organización contra ataques de spam, suplantación de identidad, phishing y malware. Familiarizarte con las diversas opciones de enrutamiento de correo disponibles.
• Conocer las prácticas recomendadas de Google para implementar Google Workspace en tu organización.

Módulo 1: Introduction to Google Workspace Administration

• Set up a Google Workspace account.
• Access and navigate the Google Workspace Admin console.
• Describe the key properties of the Google Workspace Directory.
• Explain how an organizational structure can be used to simplify user and service management.
• Provision users, groups, and calendar resources in Google Workspace, and undertake common user management tasks.
• Describe the types of admin roles available in Google Workspace.

Módulo 2: Managing Google Workspace

• Describe how to manage and configure Google Workspace services for an organization.
• Explore the mobile device management options available in Google Workspace.
• Describe Google Vault and learn how to use it to retain, search, and export your organization’s data.
• Navigate Google Workspace admin reports and set up administrator alerts.

Módulo 3: Google Workspace Security

• Configure Google Workspace’s user protection settings, such as password policies and recovery.
• Implement 2-step verification in your organization.
• Describe the SSO options available in Google Workspace.
• Identify the differences between using Google as an identity provider versus using a third-party provider.
• Use Secure LDAP to integrate LDAP compliant applications.
• Configure trusted applications for your organization.
• Configure access to trusted applications to prevent malicious attacks.
• Use the security center to identify, triage, and act on security and privacy issues.

Módulo 4: Google Workspace Mail Management

• Explain the Domain Name System (DNS), and identify the common record types.
• Implement Google Workspace’s email security measures, and explain the purpose of each.
• Configure Google Workspace to protect users from spam, phishing, and malware attacks.
• Implement email compliance features provided by Google Workspace.
• Describe common Google Workspace email routing options.

Módulo 5: Planning for a Google Workspace Deployment

• Describe Google’s three-phase deployment methodology.
• Describe how to provision domains, users, groups, and other objects in Google Workspace.
• Describe mail delivery types, and explain how routing should be configured during each deployment phase.
• Explain migration options, and make recommendations based on organizational needs.
• Describe how to migrate mail, calendar, and contacts data from a legacy platform to Google Workspace.
• Explain coexistence challenges encountered during a Google Workspace deployment, and recommend best practices, in particular around calendar coexistence.
• Describe the value of change management and the importance of the change manager.

Documentación oficial para el curso Google Workspace Administration.

• 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.

• Observar y supervisr los servicios que se ejecutan en los clústeres de Anthos en varios entornos.
• Instalar Anthos Service Mesh en clústeres de Anthos en varios entornos.
• Configurar el enrutamiento del tráfico, mejorar la seguridad de la red y la visibilidad con Anthos Service Mesh.
• Crear arquitecturas de red de varios clústeres con Anthos Service Mesh.

Módulo 1: Introducing Anthos Service Mesh

Temas:

• 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

Objetivos:

• Understand the benefits of Anthos Service Mesh, including running distributed services across clusters and enhancing service observability, traffic management, and security.
• Install Anthos Service Mesh on different Anthos clusters and choose capabilities depending on the level of management and automation you want.
• Collect workload telemetry including metrics, traces, and logs, and learn to visualize your services on the Anthos Service Mesh dashboards.
• Understand Anthos Service Mesh and the capabilities,

Módulo 2: Managing Traffic Flow with Anthos Service Mesh

Temas:

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

Objetivos:

• Understand how Anthos Service Mesh learns the network from Kubernetes and builds on top to provide advanced routing capabilities.
• Deploy mesh API resources such as the VirtualService, DestinationRule, Gateway, Service Entry, and the sidecar to configure the mesh.
• Harden the mesh network by introducing new functionality such as request retries, request timeouts, and circuit breakers.
• Test the mesh network by creating failures and delays on specific services to improve the overall resilience.

Módulo 3: Securing Network Traffic with Anthos Service Mesh

Temas:

• 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

Objetivos:

• Encrypt traffic between microservices to prevent anyone in the network from accessing private information.
• Authorize services and requests; ensuring that services only access the information that is allowed access from other services.
• Authenticate services and requests to verify trust among services in the mesh and among end users.
• Limit service access in the network so

Módulo 4: Multi-Cluster Networking with Anthos Service Mesh

Temas:

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

Objetivos:

• Understand how to do multi-cluster networking, both north-south and east-west routing, with different network configurations.
• Learn how to configure east-west networking on different Anthos clusters running on multi-cloud and hybrid locations with Anthos Service Mesh.
• Install Anthos Service Mesh on different Anthos GKE clusters, and choose the right network configuration depending on where you want to run your cluster.
• Combine Anthos Service Mesh with multi-cluster Gateways and multi-cluster services (MCS) to seamlessly run distributed services.

Documentación oficial para el curso Cloud Operations and Service Mesh with Anthos.

• 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.

• Líderes de TI y líderes empresariales interesados en adoptar la filosofía SRE. Los roles incluyen, entre otros: CTO, director/gerente de TI, vicepresidente/director/gerente de ingeniería.
• Otros roles de productos y TI, como gerentes o ingenieros de operaciones, ingenieros de software, gerentes de servicios o gerentes de productos.

• Conocer la importancia del SRE para el éxito del proyecto de transformación de TI de una organización.
• Conocer la relación entre DevOps y SRE.
• Conocer los pilares de DevOps.
• Saber cómo se alinean las prácticas de SRE con los pilares de DevOps.
• Comprender el valor que SRE puede proporcionar a una organización.
• Conocer los fundamentos técnicos y culturales de la SRE.
• Aprender dónde se puede aplicar SRE dentro de las operaciones de TI.
• Conocer las habilidades que necesita una SRE.
• Conocer los diferentes tipos de implementaciones de equipos SRE.

Módulo 1: Welcome to Developing a Google SRE Culture

Explain why SRE is important to an organization’s IT transformation project’s success.

Módulo 2: DevOps, SRE, and Why They Exist

• Distinguish between DevOps and SRE.
• Articulate the pillars of DevOps.
• Explain how SRE practices align to DevOps pillars.

Módulo 3: SLOs with Consequences

• Understand the value SRE can provide to an organization.
• Describe the technical fundamentals of SRE (SLOs, error budgets, and blameless postmortems).
• Describe the cultural fundamentals of SRE (Psychological safety, blamelessness, unified vision, collaboration, and knowledge sharing).

Módulo 4: Make Tomorrow Better than Today

• Describe the technical fundamentals of SRE (continuous integration/continous delivery, canarying, and toil automation).
• Describe the cultural fundamentals of SRE (design thinking, prototyping, psychology of change, and resistance to change).

Módulo 5: Regulate Workload

• Describe the technical fundamentals of SRE (measuring toil and reliability, and monitoring).
• Describe the cultural fundamentals of SRE (goal-setting, transparency, data-driven decision making).

Módulo 6: Apply SRE in Your Organization

• Assess their organization’s SRE maturity level.
• Identify where SRE can be applied within their business.
• Recognize the skills an SRE needs.
• Articulate the different types of SRE team implementations.
• Advocate for SRE culture adoption across their organization.

Módulo 7: Final Assessment

Assess SRE technical and cultural fundamentals knowledge.

Documentación oficial para el curso Developing a Google SRE Culture.

• 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.

• Identificar los desafíos de la carga de trabajo abordados y los componentes involucrados en las soluciones informáticas sin servidor en Anthos.
• Instalar cargas de trabajo en Cloud Run y Cloud Run para Anthos.
• Configurar y revisar el registro, las métricas y la supervisión de las soluciones informáticas sin servidor en Anthos.
• Identificar las fases de la migración y los tipos de carga de trabajo para las migraciones con Anthos.
• Instalar Migrate for Anthos.
• Migrar cargas de trabajo.
• Conocer las mejores prácticas al migrar con Anthos.
• Aprender múltiples estrategias para la implementación de aplicaciones automatizadas y la integración y entrega continuas (CI/CD) con Anthos.

Módulo 1: Application Migration with Anthos

Temas:

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

Objetivos:

• Understand the benefits and challenges of containerizing applications.
• Determine which workloads are a good or bad fit for migrations.
• Use automated tools to assess a workload’s containerization fitness of a workload.
• Plan a workload migration.

Módulo 2: Modern CI/CD for Anthos

Temas:

• 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 third-party software

Objetivos:

• Recall the application deployment goals when working with microservicebased architectures.
• Identify the model and benefits of Cloud Marketplace.
• Explain manual (non-Marketplace) deployment patterns with Anthos Clusters.
• Describe an automated CI/CD architecture using Cloud Build.

Módulo 3: Serverless on Anthos

Temas:

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

Objetivos:

• Identify workload challenges addressed by serverless computing solutions.
• List the Anthos serverless computing solutions components.
• Install Cloud Run for Anthos.
• Deploy, update, and adjust serverless workloads on Anthos.
• Use Eventarc to deliver events.
• Use best practices when running Serverless Computing Solutions on Anthos.

Documentación oficial para el curso Hybrid Cloud Modernizing Applications with Anthos.

• 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.

• Científicos de datos que busquen generar un impacto comercial mediante la conversión rápida del prototipo de machine learning a la producción.
• Ingenieros de software que busquen desarrollar habilidades de ingeniería de machine learning.
• Ingenieros de machine learning que deseen adoptar Google Cloud.

• Organizar el entrenamiento y la implementación de modelos con TFX y Cloud AI Platform.
• Operar modelos de machine learning implementados de manera efectiva y eficiente.
• Realizar una capacitación continua con varios marcos (Scikit Learn, XGBoost, PyTorch) y organizar pipelines con Cloud Composer y MLFlow.
• Integre los flujos de trabajo de machine learning con los flujos de trabajo de gestión de datos ascendentes y descendentes para mantener la gestión integral de linaje y metadatos.

Módulo 1: Introduction to TFX

• Develop a high level understanding of TFX standard pipeline components.
• Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
• Work with the Tensorflow Data Validation (TFDV) library to check and analyze input data.
• Utilize the Tensorflow Transform (TFT) library for scalable data preprocessing and feature transformations.
• Use the KerasTuner library for model hyperparameter tuning.
• Employ the Tensorflow Model Analysis (TFMA) library for model evaluation.

Módulo 2: Pipeline orchestration with TFX

Use the TFX CLI and Kubeflow UI to build and deploy TFX pipelines to a hosted AI Platform Pipelines instance on Google Cloud.

• Deploy a TensorFlow model trained using AI Platform Training to AI Platform Prediction.
• Perform advanced distributed hyperparameter tuning using CloudTuner and Cloud AI Platform Vizier.

Módulo 3: Custom components and CI/CD for TFX pipelines

Develop a CI/CD workflow with Cloud Build to build and deploy a TFX Pipeline.

• Integrate Github trigger to trigger Cloud Build CI/CD workflow for a TFX pipeline.

Módulo 4: ML Metadata with TFX

Access and analyze pipeline artifacts in ML Metadata store.

Módulo 5: Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

Perform continuous training with Scikit-learn and AI Platform Pipelines

• Perform continuous training with PyTorch and AI Platform Pipelines
• Perform continuous training with XGBoost and AI Platform Pipelines
• Perform continuous training with TensorFlow and AI Platform Pipelines

Módulo 6: Continuous Training with Cloud Composer

Perform continuous training with Cloud Composer

Módulo 7: ML Pipelines with MLflow

Manage Machine Learning lifecycle with MLflow

Módulo 8: Summary

Summarize the course

Documentación oficial para el curso ML Pipelines on 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.

• Científicos de datos que busquen pasar rápidamente del prototipo de machine learning a la producción para lograr un impacto comercial.
• Ingenieros de software que busquen desarrollar habilidades de ingeniería de machine learning.
• Ingenieros de machine learning que deseen adoptar Google Cloud.

• Identificar y utilizar las tecnologías básicas necesarias para respaldar MLOps efectivos.
• Configurar y aprovisionar arquitecturas de Google Cloud para entornos de MLOps confiables y efectivos.
• Implementar flujos de trabajo de inferencia y capacitación confiables y repetibles.
• Adoptar las mejores prácticas de CI/CD en el contexto de los sistemas de ML.
• Operar modelos de machine learning implementados de manera efectiva y eficiente.

Módulo 1: Why and When do we need MLOps

• Discuss Data Scientists’ pain points
• Identify ML Engineering characteristics and challenges
• Define how Google Cloud can help with MLOps
• Recognize how MLOps differs from manual ML management
• Compare and contrast DevOps vs MLOps

Módulo 2: Understanding the Main Kubernetes Components (Optional)

• Define what is a Docker container
• Create Docker containers
• Identify the architecture of Kubernetes: pods, namespaces
• Create Docker containers using Google Container Builder
• Store container images in Google Container Registry
• Create a Kubernetes Engine cluster
• Manage Kubernetes deployments

Módulo 3: Introduction to AI Platform Pipelines

• Identify the benefits and opportunities of AI Pipelines
• Define Access Controls within AI Pipelines
• Recognize pipeline components
• List pipeline workflows
• Set up AI Platform Pipelines
• Create a machine learning pipeline
• Run a machine learning pipeline
• Connect to AI Platform Pipelines using the Kubeflow Pipelines SDK
• Configure a Google Kubernetes Engine cluster for AI Platform Pipelines

Módulo 4: Training, Tuning and Serving on AI Platform

• Identify the main concepts of MLOps on AI Platform
• Create a reproducible dataset
• Implement a tunable model
• Build and push a training container
• Train and tune a model
• Serve and query a model

Módulo 5: Kubeflow Pipelines on AI Platform

• Recognize how Kubeflow Pipelines fits in MLOps
• Describe a Kubeflow Pipeline with KF DSL
• Use the various Kubeflow components
• Compile, upload, and run a pipeline build in Kubeflow Pipelines

Módulo 6: CI/CD for Kubeflow Pipelines on AI Platform

• Create Cloud Build Builders
• Configure pipelines with Cloud Build
• Create triggers for training models using Cloud Build Triggers
• Adopt the best CI/CD practices in the context of ML systems

Módulo 7: Summary

• Summarize the course

Documentación oficial para el curso MLOps (Machine Learning Operations) 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.

• Ingenieros de datos y programadores interesados en aprender a poner en práctica el machine learning.
• Cualquier persona interesada en aprender a construir y poner en funcionamiento modelos de TensorFlow.

• Implementar los diversos tipos de sistemas de producción de ML: capacitación estática, dinámica y continua, inferencia estática y dinámica, y procesamiento por lotes y en línea.
• Resolver un problema de ML mediante la creación de una canalización integral, desde la exploración de datos, el preprocesamiento, la ingeniería de características, la creación de modelos, el ajuste de hiperparámetros, la implementación y el servicio.
• Desarrollar una variedad de modelos de clasificación de imágenes, desde modelos lineales simples hasta redes neuronales convolucionales (CNNs) de alto rendimiento con normalización por lotes, aumento y transferencia de aprendizaje.
• Pronosticar valores de series temporales utilizando CNN, redes neuronales recurrentes (RNNs) y LSTMs.
• Aplicar ML al texto en lenguaje natural utilizando CNNs, RNNs, LSTMs, incrustaciones de palabras reutilizables y modelos generativos de codificador y decodificador.
• Implementar modelos de recomendación en TensorFlow, basados en contenido, colaborativos, híbridos y neuronales.

Módulo 1: End-to-End Machine Learning with TensorFlow on GCP

Temas:

In the first course of this specialization, we recap what was covered in the Machine Learning on Google Cloud Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is run like a workshop where you will carry out end-toend machine learning with TensorFlow on Google Cloud Platform. Here you will learn how to explore large datasets for features, create training and evaluation datasets, build models with the Estimator API in TensorFlow, train at scale and deploy those models into production with Google Cloud Platform machine learning tools.

New learners with ML background can also follow this course to learn how to do ML on GCP to fast track to the more advanced topics coming soon under the advanced specialization.

Módulo 2: Production ML Systems

Temas:

We’ll cover how to implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. We’ll delve into TensorFlow abstraction levels and the various options for doing distributed training and how to write distributed training models with custom estimators.

• Compare static vs. dynamic training and inference
• Manage model dependencies
• Set up distributed training for fault tolerance, replication, and more
• Export models for portability

Módulo 3: Image Classification Models

Temas:

We will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while avoiding overfitting our data.

• Classify images using deep learning
• Implement convolutional neural networks
• Improve the model by augmentation, batch normalization, etc.
• Leverage transfer learning

Objetivos:

Gain an overview of how ML is applied to image classification, including the evolving methods and challenges

Módulo 4: Sequence Models

Temas:

• Predict future values of a time-series
• Classify free form text
• Address time-series and text problems with recurrent neural networks
• Choose between RNNs/LSTMs and simpler models
• Train and reuse word embeddings in text problems

Objetivos:

This module is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.

Módulo 5: Recommendation Models

Temas:

• Devise a content-based recommendation engine
• Implement a collaborative filtering recommendation engine
• Build a hybrid recommendation engine with user and content embeddings

Objetivos:

Apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

Documentación oficial para el curso Advanced Machine Learning with TensorFlow on Google.

• 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.

• Ingenieros que planeen una migración de datos a Google Cloud Platform (GCP)
• Ingenieros que trabajen en un proyecto de migración de base de datos.
• Gerentes técnicos, tomadores de decisiones de TI y otros que deseen comprender los beneficios, riesgos, recompensas y procesos de migrar bases de datos a la nube.

• Planificar, ejecutar, probar y supervisar migraciones de bases de datos empresariales simples y complejas a Google Cloud.
• Evaluar arquitecturas de bases de datos locales y planificar migraciones a implementaciones optimizadas para la nube.
• Seleccionar los destinos de base de datos de Google Cloud apropiados en función de las fuentes de datos locales.
• Migrar bases de datos de SQL Server a Cloud SQL y Compute Engine.
• Ejecutar bases de datos de Oracle en Google Cloud bare metal.
• Reconocer y superar los desafíos del mundo real de mover datos para evitar la pérdida de datos, preservar la integridad de los datos y minimizar el
• tiempo de inactividad.
• Probar y monitorear proyectos de migración de datos.
• Aprovechar las herramientas para automatizar la migración de datos.
• Presentar caso comercial para trasladar las bases de datos a Google Cloud.

Módulo 1: Migrating Enterprise Databases to the Cloud

• Get a high-level solution overview of use cases, customers, and competitors.
• Understand traditional database architectures.
• Optimize databases for the cloud.
• Architect cloud databases for high-availability, scalability, and durability.

Módulo 2: Google Cloud Data Migration Solutions

• Evaluate the database solutions available on Google Cloud.
• Run databases on Google Cloud infrastructure using Compute Engine.
• Leverage Kubernetes and GKE for deploying databases.
• Use Cloud SQL for managed database solutions.
• Provision Bare Metal Solution for Oracle databases.
• Estimate the cost of database solutions.

Módulo 3: Google Implementation Methodology

• Migrate to the cloud using Google’s implementation methodology
• Perform the key database migration activities
• Choose the appropriate database migration approach.

Módulo 4: Migration Strategies

• Lift and shift databases from on-premises to Google Cloud.
• Backup and restore databases from on-premises to Google Cloud services.
• Migrate databases to the cloud with no downtime.
• Optimize databases for the cloud.

Módulo 5: Networking for Secure Database Connectivity

• Build secure networks to host databases and database client applications.
• Allow secure communication across networks using VPC Peering, VPNs, and interconnect.
• Control access to databases using firewall rules.
• Automate network infrastructure using Terraform.

Módulo 6: Migrating SQL Server Databases to Google Cloud

• Lift and shift SQL Server databases using Compute Engine.
• Employ Cloud SQL for managed SQL Server databases.
• Architect SQL Server for security, high availability, and disaster recovery.
• Configure SQL Server to run with Kubernetes on GKE.

Módulo 7: Migrating Oracle Databases to Google Cloud

• Explain why running Oracle on Google Cloud makes sense.
• Review the technical specs of Oracle BMS.
• Define common use cases for running Oracle on Google Cloud.

Módulo 8: Testing and Monitoring Databases in Google Cloud

• Use unit, integration, and regression testing techniques to ensure database migration success.
• Monitor your migration projects with Google tools.

Módulo 9: Google Cloud Data Migration Tools

• Move large amounts of data to the cloud using Google transfer services
• Program data processing and ETL pipelines using Cloud Data Fusion
• Create workflows using Composer

Módulo 10: Making the Business Case for Moving to Google Cloud

• Write a business case to justify a database migration.
• Perform risk and cost/benefit analysis on a cloud migration project.
• Estimate the costs associated with database migration.

Documentación oficial para el curso Enterprise Database Migration.

• 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.

Obtener la certificación Cloud Digital Leader

Obtener la certificación Professional Google Workspace Administrator

Obtener la certificación Professional Data Engineer

Obtener la certificación Professional Machine Learning Engineer

Obtener la certificación Professional Cloud DevOps Engineer

Obtener la certificación Professional Cloud Developer

Obtener la certificación Professional Cloud Database Engineer

Obtener la certificación Professional Cloud Security Engineer

Obtener la certificación Professional Cloud Network Engineer

Obtener la certificación Professional Cloud Architect

• Describir los cinco dominios que se incluyen en el examen de certificación de Professional Cloud Architect.
• Identificar brechas en sus conocimientos y habilidades con respecto a cada dominio y crear un plan de estudio.
• Prepararte para el examen de certificación Professional Cloud Architect.

Módulo 00: Introduction to the Professional Cloud Architect Certification

• Temas:
  • Role of a Professional Cloud Architect and scenario introduction*
  • Certification value and benefits
  • Certification process
• Objetivos:
  • Describe the purpose and benefits of the PCA certification.
  • Explain the certification process.

Módulo 01: Designing and planning a cloud solution architecture

• Temas:
  • Scenario: Defining initial technical and business design considerations for Cymbal’s migration to a cloud solution
• Objetivos:
  • Identify considerations involved in designing solution infrastructure that meets business and technical requirements.
  • Describe options for designing network, storage, and compute resources leveraging Google Cloud.
  • Explain the elements of a migration plan.
  • Recognize the potential for future solution improvements.
  • Determine the skills you need to develop in order to design and plan solution architecture leveraging Google Cloud.

Módulo 02: Managing and provisioning a solution infrastructure

• Temas:
  • Scenario: Provisioning and deploying the cloud solution per Cymbal’s requirements
• Objetivos:
  • Identify considerations involved in configuring network topologies, individual storage systems, and compute systems.
  • Determine the skills you need to develop in order to manage and provision solution infrastructure.

Módulo 03: Designing for security and compliance

• Temas:
  • Scenario: Identifying the security and compliance considerations pertinent to Cymbal’s cloud solution
• Objetivos:
  • Explain recommended design practices to ensure security across a cloud solution.
  • Recognize aspects of designing for compliance.
  • Determine the skills you need to develop in order to apply Google’s recommended approach to designing for security and compliance.

Módulo 04: Analyzing and optimizing technical and business processes

• Temas:
  • Scenario: Analyze costs, KPIs, etc. and identify opportunities for optimization
• Objetivos:
  • Describe the technical and business processes involved in solution production.
  • Identify ways to optimize processes and develop procedures to ensure reliability of solutions in production.
  • Determine the skills you need to develop to optimize technical and business processes involved in producing a cloud solution.

Módulo 05: Managing implementation/Ensuring solution operations and reliability

• Temas:
  • Scenario: How the PCA helps establish best practices for Cymbal’s solution deployment and ongoing operations
• Objetivos:
  • Describe best practices for development and operations teams to ensure successful solution deployment.
  • Explain methods to interact with Google Cloud programmatically.
  • Explain methodologies for managing configuration and code updates and tools available for monitoring and analyzing KPIs.

Módulo 06: Your Next Steps

• Temas:
  • Learn how to break down a case study to pinpoint business and technical requirements, and find differentiators
  • Finalizing your study plan
  • Creating a weekly study sheet
  • Registering for the exam
• Objetivos:
  • Create a personalized study plan.
  • Review next steps.

Documentación oficial para el curso Preparing for Your Professional Cloud Architect Journey.

• 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.

Obtener la certificación Associate Cloud Engineer

• Ingenieros de la nube, ingenieros de DevOps.
• Personas que deseen comenzar a usar Terraform para automatizar el aprovisionamiento de infraestructura con un enfoque en Google Cloud Platform.

• Definir la necesidad comercial de infraestructura como código y los beneficios de usarla en su entorno.
• Explicar las características y funcionalidades de Terraform.
• Utilizar recursos, variables y outputs de Terraform para crear recursos de infraestructura de Google Cloud.
• Utilizar módulos de Terraform para crear configuraciones reutilizables.
• Explicar el estado de Terraform y su importancia.

Módulo 1: Introduction to Terraform for Google Cloud

• Temas:
  • Introduction to IaC
    • What is infrastructure as code (IaC)?
    • Problems IaC can solve
    • Benefits of IaC
    • Provisioning versus configuration
    • Imperative versus declarative approach
  • Introduction to Terraform
    • Terraform overview
    • Terraform features
    • IaC configuration workflow
    • Terraform use cases
  • Using Terraform
    • How to use Terraform
    • Running Terraform in production
    • Installing Terraform
    • Authentication for Google Cloud

• Objetivos:
  • Define infrastructure as code.
  • Explain the features and benefits of using Terraform.
  • Explain the use case of Terraform for Google Cloud.
  • Describe how to use Terraform for Google Cloud.

Módulo 2: Terms and concepts

• Temas:
  • The Author phase
    • Terraform Directory structure
    • Introduction to HCL syntax
    • Resources
    • Variables
    • State
    • Modules
  • Terraform commands
    • terraform init
    • terraform plan
    • terraform apply
    • terraform fmt
    • terraform destroy
  • Terraform Validator tool
    • Introduction
    • Why use the Terraform Validator tool
    • Validation workflow
    • Terraform Validator use cases
• Objetivos:
  • Explain the Terraform workflow.
  • Create basic configuration files within Terraform.
  • Explain the purpose of a few Terraform commands.
  • Describe the Terraform Validator tool.
  • Create, update, and destroy Google Cloud resources using Terraform.

Módulo 3: Writing Infrastructure Code for Google Cloud

• Temas:
  • Introduction to Resources
    • Resources overview
    • Syntax
    • Example
    • Refer a resource attribute
  • Considerations to define a resource block
  • Meta-arguments for resources
  • Resource dependencies
    • Implicit dependency
    • Explicit dependency
  • Introduction to Variables
    • Overview
    • Syntax to declare a variable
    • Syntax to reference and assign a value to a variable
    • Variables best practices
  • Introduction to output values
    • Output values overview
    • Best practices
  • Terraform Registry and CFT
    • Introduction to Terraform Registry
    • Introduction to CFT
• Objetivos:
  • Declare the resources within Terraform.
  • Explain implicit and explicit resource dependencies.
  • Use variables and output values within the root configuration.
  • Explain Terraform Registry and Cloud Foundation Toolkit.

Módulo 4: Organizing and Reusing Configuration with Terraform Modules

• Temas:
  • Introduction to modules:
    • Why are modules needed
    • What is a module?
    • Example
  • Reusing configurations by using modules
    • Module sources
    • Calling a module into the source configuration
  • Using variables to parameterize your configuration
  • Pass resource attributes using output variables
  • Module use cases, benefits, and best practices
• Objetivos:
  • Define Terraform modules.
  • Use modules to reuse configurations.
  • Use modules from the public registry.
  • Use input variables to parameterize configurations.
  • Use output values to access resource attributes outside the module.

Módulo 5: Introduction to Terraform State

• Temas:
  • Introduction to Terraform state
    • How information is stored in a Terraform state file
  • Ways to save a state file
  • Storing a state file in a Cloud Storage bucket
    • Issues when storing the Terraform state locally
    • Benefits of storing a state file in a Cloud Storage bucket
    • Process of storing a Terraform state file remotely in a Cloud Storage bucket
  • Terraform state best practices
• Objetivos:
  • Define Terraform state.
  • List the benefits of storing the state file remotely.
  • Explain how to store the Terraform state in a Cloud Storage bucket.
  • Explain Terraform state best practices.

Documentación oficial para el curso Getting Started with Terraform for 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.

• Describir los cinco dominios que se incluyen en el examen de certificación de Associate Cloud Engineer.
• Identificar brechas en sus conocimientos y habilidades con respecto a cada dominio y crear un plan de estudio.
• Prepararte para el examen de certificación Associate Cloud Engineer.

Módulo 00: Introduction to the Associate Cloud Engineer Certification

• Temas:
  • Role of an Associate Cloud Engineer and scenario introduction*
  • Certification value and benefits
  • Certification process
• Objetivos:
  • Describe the purpose and benefits of the ACE certification.
  • Explain the certification process.

Módulo 01: Setting up a Cloud Environment

• Temas:
  • Scenario: starting the process to migrate from on-premises to Google Cloud by setting up a solution environment.
  • Creating projects, assigning IAM roles, enabling APIs, provisioning logging/ monitoring workspaces, managing billing, installing command-line tools.
• Objetivos:
  • Describe tasks involved in setting up a cloud solution environment when migrating from on-premises to Google Cloud.
  • Determine the skills you need to develop to set up a solution environment in Google Cloud.

Módulo 02: Planning and Configuring a Cloud Solution

• Temas:
  • Scenario: understanding the Cloud Engineer’s role in planning and configuring a solution in Google Cloud.
  • Estimating pricing, planning compute, data storage, and network resources.
• Objetivos:
  • Identify the tasks of the Cloud Engineer in planning and configuring a cloud solution.
  • Determine the skills you need to develop when planning and configuring a solution in Google Cloud.

Módulo 03: Deploying and Implementing a Cloud Solution

• Temas:
  • Scenario: continuing to explore the Associate Cloud Engineer’s role in helping the organization explore options for deploying and implementing a solution.
  • Differentiating and deploying among compute options including Compute Engine, GKE, App Engine, Cloud Run, and Cloud Functions.
  • Differentiating and deploying among storage and data options including Cloud Storage, Cloud SQL, Cloud Bigtable, Cloud Spanner, BigQuery, and Pub/Sub.
  • Differentiating and deploying among networking options including VPC, firewall rules, interconnect options (VPN), and load balancer options.
• Objetivos:
  • Identify the tasks of the Cloud Engineer in deploying and implementing a cloud solution.
  • Determine the skills you need to develop related to deploying and implementing a cloud solution.

Módulo 04: Ensuring Successful Operation of a Cloud Solution

• Temas:
  • Scenario: now that the organization’s cloud solution has been deployed, continue to explore the Associate Cloud Engineer’s role in operations of the cloud solution.
  • Managing and adjusting the configuration of compute resources, and understanding cost implications.
  • Managing and adjusting the configuration of storage and data resources, and understanding cost implications.
  • Managing and adjusting the configuration of network resources, and understanding cost implications.
• Objetivos:
  • Identify the tasks of the Cloud Engineer in ensuring successful operation of a cloud solution.
  • Determine the skills you need to develop to ensure successful operation of cloud solutions.

Módulo 05: Configuring Access and Security

• Temas:
  • Scenario: configuring access is a key part of administering a cloud solution. This final section of the scenario introduces the Associate Cloud Engineer’s role in configuring access and security of project resources.
  • Managing IAM and service accounts.
  • Viewing project audit logs.
• Objetivos:
  • Identify the tasks of the Cloud Engineer in configuring access and security.
  • Determine the skills you need to develop to manage access and security in Google Cloud.

Módulo 06: Your Next Steps

• Temas:
  • Finalizing your study plan
  • Creating a weekly study sheet
  • Registering for the exam
• Objetivos:
  • Create a personalized study plan.
  • Review next steps.

Documentación oficial para el curso Preparing for Your Associate Cloud Engineer Journey.

• 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.

Database administrators, engineers/developers, and cloud architects who want to learn how to create, optimize, and manage Spanner databases and migrate existing databases into Spanner.

• Build scalable, managed, relational databases using Google Cloud Spanner.
• Create and manage Spanner databases using the CLI, Terraform, Python API, and the Cloud Console.
• Optimize relational database schemas for Spanner's distributed database model.
• Leverage Google Cloud tools for administering Spanner databases and managing data.
• Program and run queries and transactions using the Spanner API.
• Integrate Spanner with your applications.

Lecture

Module 1: The Need for Spanner

• What is Spanner?
• Spanner and the CAP Theorem
• History of Spanner
• Cloud Spanner Use Cases

Module 2: Getting Started with Spanner

• Planning Spanner Instances
• Automating Instance Creating
• Creating Databases in Spanner

Module 3: Optimizing Spanner Schemas

• Spanner Architecture
• Choosing Primary Keys
• Defining Database Schemas in Spanner
• Understanding Interleaving and Foreign Keys
• Understanding Secondary Indexes

Module 4: Programming Spanner Applications, Queries, and Transactions

• Authentication and Authorization
• Using the Spanner Client Libraries
• Running Queries
• Managing Transactions

Module 5: Deploying Spanner Applications

• Using Spanner from Applications
• Building Data Pipelines into and out of Spanner

Module 6: Spanner Administration

• Managing your Data in Spanner
• Managing Change
• Operations

Module 7: Capstone Project

Lab

Creating Spanner Instances and Databases (Console)

• Compare Spanner configuration options.
• Create Spanner databases using the Console.
• Create Spanner databases using the PostgreSQL dialect.

Creating Spanner Instances and Databases (CLI and Terraform)

• Create instances and databases using the gcloud CLI.
• Automate Spanner infrastructure using Terraform.

Choosing Primary Keys

• Generate Spanner primary keys as UUIDs.
• Convert counters and timestamps into values appropriate for Spanner primary keys.

Managing Relationships with Foreign Keys and Interleaved Tables

• Create a relational database with proper primary keys and relationships optimized for Spanner
• Leverage indexes to improve read performance in Spanner

Programming Spanner Applications with Python

• Use Python to create and delete Spanner instances and databases.
• Program Spanner databases that use the PostgreSQL dialect.

Running Queries and Transactions

• Run parameterized queries using indexes against a Spanner database using the Python Client library.
• Execute transactions against a Spanner database.

Deploying Spanner Applications with Cloud Functions and Cloud Run

• Deploy Cloud Functions that read and write to Spanner databases.
• Set up and use the Spanner emulator for development.
• Build a REST API that allows you to read and write Spanner data.
• Deploy a REST API to Google Cloud Run.

Migrating Data to and from Spanner with Dataflow

• Write ETL pipelines using Apache Beam.
• Run Apache Beam pipelines using Google Cloud Dataflow.

Leverage the Autoscaler Tool for Cloud Spanner to Achieve Workload Elasticity

• Configure the Autoscaler and environment
• Deploy the Autoscaler
• Observe the autoscaling

Challenge Lab: Administering a Spanner Database

• Create a Spanner database and import existing data.
• Backup and restore the database.
• Export Spanner data and import it into BigQuery.
• Deploy a data access API that allows access to your Spanner database.
• Use the Operations monitoring tools for dashboards, uptime checks, and alerts.

• 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 la nube, administradores y personal de SysOps.
• Desarrolladores de la nube y personal de DevOps.

• Planificar e implementar una infraestructura de registro y monitoreo bien diseñada.
• Definir indicadores de nivel de servicio (SLIs) y objetivos de nivel de servicio (SLOs).
• Crear dashboards y alertas de monitoreo efectivos.
• Supervisar, solucionar problemas y mejorar la infraestructura de Google Cloud.
• Analizar y exportar los registros de auditoría de Google Cloud.
• Encontrar defectos en el código de producción, identificar cuellos de botella y mejorar el rendimiento.
• Optimizar los costos de monitoreo.

Módulo 1: Introduction to Google Cloud Monitoring Tools

• Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring.
• Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler.

Módulo 2: Avoiding Customer Pain

• Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation.
• Measure customer pain with SLIs.
• Define critical performance measures.
• Create and use SLOs and SLAs.
• Achieve developer and operation harmony with error budgets.

Módulo 3: Alerting Policies

• Develop alerting strategies.
• Define alerting policies.
• Add notification channels.
• Identify types of alerts and common uses for each.
• Construct and alert on resource groups.
• Manage alerting policies programmatically.

Módulo 4: Monitoring Critical Systems

• Choose best practice monitoring project architectures.
• Differentiate Cloud IAM roles for monitoring.
• Use the default dashboards appropriately.
• Build custom dashboards to show resource consumption and application load.
• Define uptime checks to track aliveness and latency.

Módulo 5: Configuring Google Cloud Services for Observability

• Integrate logging and monitoring agents into Compute Engine VMs and images.
• Enable and use Kubernetes Monitoring.
• Extend and clarify Kubernetes monitoring with Prometheus.
• Expose custom metrics through code and with the help of OpenCensus.

Módulo 6: Advanced Logging and Analysis

• Identify and choose among resource tagging approaches.
• Define log sinks (inclusion filters) and exclusion filters.
• Create metrics based on logs.
• Define custom metrics.
• Use Error Reporting to link application errors to Logging.
• Export logs to BigQuery.

Módulo 7: Monitoring Network Security and Audit Logs

• Collect and analyze VPC Flow logs and Firewall Rules logs.
• Enable and monitor Packet Mirroring.
• Explain the capabilities of Network Intelligence Center.
• Use Admin Activity audit logs to track changes to the configuration or metadata of resources.
• Use Data Access audit logs to track accesses or changes to user-provided resource data.
• Use System Event audit logs to track GCP administrative actions.

Módulo 8: Managing Incidents

• Define incident management roles and communication channels.
• Mitigate incident impact.
• Troubleshoot root causes.
• Resolve incidents.
• Document incidents in a post-mortem process.

Módulo 9: Monitoring Network Security and Audit Logs

• Collect and analyze VPC Flow logs and Firewall Rules logs.
• Enable and monitor Packet Mirroring.
• Explain the capabilities of Network Intelligence Center.
• Use Admin Activity audit logs to track changes to the configuration or metadata of resources.
• Use Data Access audit logs to track accesses or changes to user-provided resource data.
• Use System Event audit logs to track GCP administrative actions.

Módulo 10: Optimizing Stackdriver Costs

• Understand Stackdriver billing.
• Analyze Stackdriver resource utilization.
• Implement best practices for Stackdriver cost control.

Documentación oficial para el curso Logging, Monitoring, and Observability in 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.

• Arquitectos Cloud Solutions, ingenieros Site Reliability, profesionales SysOps, ingenieros DevOps, gerentes de TI.
• Personas que utilicen Google Cloud para crear nuevas soluciones o para integrar sistemas existentes, entornos de aplicaciones e infraestructura con Google Cloud.

• Aplicar un conjunto de herramientas de preguntas, técnicas y consideraciones de diseño.
• Definir los requisitos de la aplicación y expresarlos objetivamente como KPIs, SLOs y SLIs.
• Descomponer los requisitos de la aplicación para encontrar los límites de microservicio correctos.
• Aprovechar las herramientas para desarrolladores de Google Cloud para configurar canalizaciones de implementación modernas y automatizadas.
• Elegir los servicios de Google Cloud Storage apropiados según los requisitos de la aplicación.
• Diseñar redes en la nube y híbridas.
• Implementar aplicaciones confiables, escalables y resilientes que equilibren las métricas clave de rendimiento con el costo.
• Elegir los servicios de implementación de Google Cloud adecuados para tus aplicaciones.
• Asegurar las aplicaciones, los datos y la infraestructura en la nube.
• Supervisar los objetivos de servicio y los costos con las herramientas de Stackdriver.

Módulo 1: Defining the Service

• Describe users in terms of roles and personas
• Write qualitative requirements with user stories
• Write quantitative requirements using key performance indicators (KPIs)
• Evaluate KPIs using SLOs and SLIs
• Determine the quality of application requirements using SMART criteria

Módulo 2: Microservice Design and Architecture

• Decompose monolithic applications into microservices
• Recognize appropriate microservice boundaries
• Architect stateful and stateless services to optimize scalability and reliability
• Implement services using 12-factor best practices
• Build loosely-coupled services by implementing a well-designed REST architecture
• Design consistent, standard RESTful service APIs

Módulo 3: DevOps Automation

• Automate service deployment using CI/CD pipelines
• Leverage Cloud Source Repositories for source and version control
• Automate builds with Google Cloud Build and build triggers
• Manage container images with Google Container Registry
• Create infrastructure with code using Deployment Manager and Terraform

Módulo 4: Choosing Storage Solutions

• Choose the appropriate Google Cloud data storage service based use case, durability, availability, scalability and cost
• Storage binary data with Cloud Storage
• Store relational data using Cloud SQL and Spanner
• Store NoSQL data using Firestore and BigTable
• Cache data for fast access using Memorystore
• Build a data warehouse using BigQuery

Módulo 5: Google Cloud and Hybrid Network Architecture

• Design VPC networks to optimize for cost, security and performance
• Configure global and regional load balancers to provide access to services
• Leverage Cloud CDN to provide lower latency and decrease network egress
• Evaluate network architecture using the Network Intelligence Center
• Connect networks using peering and VPNs
• Create hybrid networks between Google Cloud and on-premises data centers

Módulo 6: Deploying Applications to Google Cloud

• Choose the appropriate Google Cloud deployment service for your applications
• Configure scalable, resilient infrastructure using Instance Templates and Groups
• Orchestrate microservice deployments using Kubernetes and GKE
• Leverage App Engine for a completely automated platform as a service (PaaS)
• Create serverless applications using Google Cloud Functions

Módulo 7: Designing Reliable Systems

• Design services to meet requirements for availability, durability and scalability
• Implement fault tolerant systems by avoiding single points of failure, correlated failures and cascading failures
• Avoid overload failures the the circuit breaker and truncated exponential backoff design patterns
• Design resilient data storage with lazy deletion
• Analyze disaster scenarios and plan for disaster recovery using cost/risk

Módulo 8: Security

• Design secure systems using best-practices like separation of concerns, principle of least privilege and regular audits
• Leverage Google Cloud Security Command Center to help identify vulnerabilities
• Simplify cloud governance using or organization policies and folders
• Secure people using IAM roles, Identity Aware Proxy and Identity Platform
• Manage the access and authorization of resources by machines and processes using service accounts
• Secure networks with with private IPs, firewalls and Google Cloud private access
• Mitigate DDoS attacks by leveraging Cloud DNS and Cloud Armor

Módulo 9: Designing Reliable Systems

• Manage new service versions using rolling updates, blue-green deployments and canary releases
• Forecast, monitor and optimize service cost using the Google Cloud pricing calculator, billing reports and by analyzing billing data
• Observe if your services are meeting their SLOs using Stackdriver Monitoring and Dashboards
• Use Uptime Checks to determine service availability
• Respond to service outages using Stackdriver Alerts

Documentación oficial para el curso Architecting with Google Cloud Design and Process.

• 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.

Este curso está dirigido, principalmente, a los siguientes participantes:

• Aspirantes a analistas de datos de machine learning, científicos de datos e ingenieros de datos.
• Personas que deseen aprender sobre machine learning mediante Vertex AI AutoML, BQML, Feature Store, Workbench, Dataflow, Vizier para el ajuste de hiperparámetros, y TensorFlow/Keras.

• Crear, entrenar e implementar un modelo de machine learning sin escribir una sola línea de código usando Vertex AI AutoML.
• Comprender cuándo usar AutoML y Big Query ML.
• Crear conjuntos de datos gestionados por Vertex AI.
• Agregar funciones a una Feature Store.
• Describir Analytics Hub, Dataplex y Data Catalog.
• Describir el ajuste de hiperparámetros con Vertex Vizier y cómo se puede utilizar para mejorar el rendimiento del modelo.
• Crear un cuaderno administrado por el usuario de Vertex AI Workbench, un trabajo de capacitación personalizado y luego implementarlo usando un contenedor Docker.
• Describir predicciones por lotes y en línea, y monitoreo de modelos.
• Describir cómo mejorar la calidad de los datos.
• Realizar análisis de datos exploratorios.
• Construir y entrenar modelos de aprendizaje supervisado.
• Optimizar y evaluar modelos utilizando funciones de pérdida y métricas de rendimiento.
• Crear conjuntos de datos de prueba, evaluación y entrenamiento repetibles y escalables.
• Implementar modelos ML usando TensorFlow/Keras.
• Describir cómo representar y transformar características.
• Comprender los beneficios de utilizar la ingeniería de funciones.
• Explicar Vertex AI Pipelines.

Curso 1: How Google Does Machine Learning

What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve?

Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.

• Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code.
• Describe best practices for implementing machine learning on Google Cloud.
• Develop a data strategy around machine learning.
• Examine use cases that are then reimagined through an ML lens.
• Leverage Google Cloud Platform tools and environment to do ML.
• Learn from Google’s experience to avoid common pitfalls.
• Carry out data science tasks in online collaborative notebooks.

Curso 2: Launching into Machine Learning

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

• Describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code.
• Describe Big Query ML and its benefits.
• Describe how to improve data quality.
• Perform exploratory data analysis.
• Build and train supervised learning models.
• Optimize and evaluate models using loss functions and performance metrics.
• Mitigate common problems that arise in machine learning.
• Create repeatable and scalable training, evaluation, and test datasets.

Curso 3: TensorFlow on Google Cloud

The modules cover designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

• Create TensorFlow and Keras machine learning models.
• Describe TensorFlow key components.
• Use the tf.data library to manipulate data and large datasets.
• Build a ML model using tf.keras preprocessing layers.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation. Understand how model subclassing can be used for more customized models.
• Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
• Train, deploy, and productionalize ML models at scale with Cloud AI Platform.

Curso 4: Feature Engineering

Want to know about Vertex AI Feature Store? Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering, where we discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

• Describe Vertex AI Feature Store.
• Compare the key required aspects of a good feature.
• Combine and create new feature combinations through feature crosses.
• Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
• Understand how to preprocess and explore features with Dataflow and Dataprep by Trifacta.
• Understand and apply how TensorFlow transforms features.

Curso 5: Machine Learning in the Enterprise

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.

The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives.

A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model.

You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker.

The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.

• Understand the tools required for data management and governance.
• Describe the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.
• Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.
• Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance.
• Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
• Describe the benefits of Vertex AI Pipelines.

Documentación oficial para el curso Machine Learning on 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.

• Ingenieros de red y administradores que utilicen Google Cloud o planeen hacerlo.
• Personas que deseen estar expuestos a soluciones de redes definidas por software en la nube.

• Configurar redes, subredes y enrutadores de VPC de Google.
• Controlar el acceso administrativo a los objetos de la VPC.
• Controlar el acceso a la red a los puntos finales en las VPC.
• Interconectar redes entre proyectos de Google Cloud.
• Interconectar redes entre redes de VPC de Google Cloud y redes locales u otras redes en la nube.
• Seleccionar entre las opciones de balanceador de carga y proxy de Google Cloud y configurarlas.
• Usas Cloud CDN para reducir la latencia y ahorrar dinero.
• Optimizar el gasto de la red con los niveles de red.
• Configurar Cloud NAT o Private Google Access para proporcionar instancias sin acceso a direcciones IP públicas a otros servicios.
• Implementar redes declarativamente utilizando Cloud Deployment Manager o Terraform.
• Diseñar redes para cumplir con los requisitos comunes de los clientes.
• Configurar la supervisión y el registro para solucionar problemas de redes.

Módulo 1: Google Cloud VPC Networking Fundamentals

• Recall that networks belong to projects
• Explain the differences among default, auto, and custom networks
• Create networks and subnets
• Explain how IPv4 addresses are assigned to Compute Engine instances
• Publish domain names using Google Cloud DNS
• Create Compute Engine instances with IP aliases
• Create Compute Engine instances with multiple virtual networks

Módulo 2: Controlling Access to VPC Networks

• Outline how IAM policies affect VPC networks
• Control access to network resources using service accounts
• Control access to Compute Engine instances with tag-based firewall rules

Módulo 3: Sharing Networks Across Projects

• Outline the overall workflow for configuring Shared VPC
• Differentiate between the IAM roles that allow network resources to be managed
• Configure peering between unrelated VPC Networks
• Recall when to use Shared VPC and when to use VPC Network Peering

Módulo 4: Load Balancing

• Recall the various load balancing services
• Configure Layer 7 HTTP(S) load balancing
• Whitelist and blacklist IP traffic with Cloud Armor
• Cache content with Cloud CDN
• Explain Layer 4 TCP or SSL proxy load balancing
• Explain regional network load balancing
• Configure internal load balancing
• Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers
• Determine which Google Cloud load balancer to use In which situation

Módulo 5: Hybrid Connectivity

• Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud
• Explain Dedicated Interconnect and Partner Interconnect
• Describe the workflow for configuring a Dedicated Interconnect
• Build a connection over a VPN with Cloud Router
• Determine which Google Cloud interconnect service to use in which situation
• Explain Direct Peering and Partner Peering
• Determine which Google Cloud peering service to use in which situation

Módulo 6: Networking Pricing and Billing

• Recognize how networking features are charged
• Use Network Service Tiers to optimize spend
• Determine which Network Service Tier to use in which situation
• Recall that labels can be used to understand networking spend

Módulo 7: Network Design and Deployment

• Explain common network design patterns
• Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses
• Configure Cloud NAT to provide your instances without public IP addresses access to the internet
• Automate the deployment of networks using Deployment Manager or Terraform
• Launch networking solutions using Cloud Marketplace

Módulo 8: Network Monitoring and Troubleshooting

• Configure uptime checks, alerting policies and charts for your network services
• Use VPC Flow Logs to log and analyze network traffic behavior

Documentación oficial para el curso Networking in 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.

• Describir los dominios que se incluyen en el examen de certificación de Professional Cloud Network Engineer.
• Identificar brechas en sus conocimientos y habilidades con respecto a cada dominio y crear un plan de estudio.
• Prepararte para el examen de certificación Professional Cloud Network Engineer.

Módulo 0: PCNE certification

Temas:

• Role of a Professional Cloud Network Engineer
• Introduction to a sample infrastructure scenario that is used throughout the course
• Certification process

Objetivos:

• Describe the purpose and benefits of the PCNE certification.
• Explain the certification process.
• Create a study plan for designing, planning, and prototyping a Google Cloud network.

Módulo 1: Designing, planning, and prototyping a Google Cloud Network

Temas:

Within the context of the sample scenario:

• Describe how Load Balancing will be used for general users accessing resources from the internet as well as internal users accessing from private connected networks.
• Describe how Cloud CDN, Armor and NAT will be configured and the benefits they will provide, and used and how Cloud DNS will be integrated with an on-premise DNS system.
• Describe how packet mirroring will be used for periodic network forensic audit.

Objetivos:

• Explain the considerations involved in designing an overall network architecture.
• Identify considerations for designing a VPC, a hybrid or multi-cloud network, or an IP addressing plan for GKE.
• Determine the skills you need to develop to effectively design, plan, and prototype a Google Cloud network.

Módulo 2: Implementing a VPC

Temas:

Within the context of the sample scenario:

• Assuming Kubernetes clusters will be used in all the networks, describe how IP addresses will be distributed among them.
• Describe the sort of interconnections will be added between Google Cloud and external environments and how the routing will be configured for them.
• Describe how the network will be secured with firewall rules and VPC Service Controls

Objetivos:

• Identify the tasks involved in configuring VPCs, configuring routing, and configuring and maintaining GKE clusters.
• Describe considerations for configuring firewalls and implementing VPC Service Controls.
• Determine the skills you need to develop related to implementing a VPC.

Módulo 3: Configuring network services

Temas:

Within the context of the sample scenario:

• Describe how Load Balancing will be used for general users accessing resources from the internet as well as internal users accessing from private connected networks.
• Describe how Cloud CDN, Armor and NAT will be configured and the benefits they will provide, and used and how Cloud DNS will be integrated with an on-premise DNS system.
• Describe how packet mirroring will be used for periodic network forensic audit

Objetivos:

• Identify considerations involved in configuring load balancing.
• Describe the scope of tasks to configure Cloud Armor policies, Cloud CDN, Cloud DNS, Cloud NAT, and network packet inspection.

Módulo 4: Implementing hybrid connectivity

Temas:

Within the context of the sample scenario: describe the details of the VPN and Interconnect links between Google Cloud VPCs and on-premise or other cloud networks.

Objetivos:

• Describe how to configure Google Cloud Interconnect, a site-to-site IPsec VPN, and Cloud Router.
• Determine the skills you need to develop to ensure successful implementation of hybrid interconnectivity.

Módulo 5: Managing, monitoring, and optimizing network operations

Temas:

Within the context of the sample scenario:

• Describe the network troubleshooting and debugging process.
• Describe the troubleshooting process using Cloud Logging and Monitoring and Network Intelligence Center.

Objetivos:

• Describe options for logging and monitoring with Google Cloud’s operations suite.
• Explain tasks involved in managing and maintaining security.
• Identify considerations for maintaining and troubleshooting connectivity issues, latency, and traffic flow.
• Determine the skills you need to develop to manage and optimize network operations with Google Cloud.

Módulo 6: Your next steps

Temas:

Based on domain knowledge deficiencies noted after answering the diagnostic questions, construct a weekly study plan.

Objetivos:

• Create a weekly study plan.
• Set weekly study goals.
• Register for the Professional Cloud Network Engineer exam.

Documentación oficial para el curso Preparing for Your Professional Cloud Network Engineer Journey.

• 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.

• Describir los dominios que se incluyen en el examen de certificación de Professional Cloud Security Engineer.
• Identificar brechas en sus conocimientos y habilidades con respecto a cada dominio y crear un plan de estudio.
• Prepararte para el examen de certificación Professional Cloud Security Engineer.

Módulo 1: Welcome to Your Professional Cloud Security Engineer Journey

Temas:

• What is the role of a Professional Cloud Security Engineer?
• Resources to support your certification journey.
• Creating your study plan.

Objetivos:

• Understand the role of a Professional Cloud Security Engineer.
• Learn about the sample scenario used throughout the course.
• Understand the benefits of becoming Google Cloud Certified.
• Understand the course structure and types of learning assessments.

Módulo 2: Configuring Access Within a Cloud Solution Environment

Temas:

• Planning Cymbal Bank’s cloud identity and access management
• Diagnostic questions
• Review and study planning

Objetivos:

• Define Resource Hierarchy and configure Cloud Identity.
• Explain considerations for managing service accounts, authentication, and authorization controls.
• Determine the skills you need to develop to use best practices for configuring access within a cloud solution environment.

Módulo 3: Configuring Network Security

Temas:

• Securing Cymbal Bank’s network resources
• Diagnostic questions
• Review and study planning

Objetivos:

• Identify considerations involved in designing network security (perimeter controls, load balancing, DNSSE, addressing, Cloud DNS).
• Describe how to configure network segmentation and establish private connectivity.
• Determine the skills you need to develop in order to design and configure network security.

Módulo 4: Ensuring Data Protection

Temas:

• Securing Cymbal Bank’s data
• Diagnostic questions
• Review and study planning

Objetivos:

• Identify practices to protect sensitive data.
• Recognize Google’s approach to managing encryption.
• Determine the skills you need to develop in order to apply Google’s approach to protect and encrypt sensitive data.

Módulo 5: Managing Operations in a Cloud Environment

Temas:

• Cymbal Bank’s security operations
• Diagnostic questions
• Review and study planning

Objetivos:

• Identify considerations involved in building and deploying secure infrastructure
  and applications.
• Describe how to design and configure effective logging, monitoring, and detection in Google Cloud.
• Determine the skills you need to develop to ensure successful ongoing security operations in a Google Cloud environment.

Módulo 6: Ensuring Compliance

Temas:

• Cymbal Bank’s security regulatory compliance
• Diagnostic questions
• Review and study planning

Objetivos:

• Identify considerations for compliance and regulatory requirements in a cloud environment.
• Determine the skills you need to develop to ensure regulatory compliance for solutions in Google Cloud.

Módulo 7: Your next steps

Temas:

• Sample study plans
• Exam registration

Objetivos:

• Learn how to create an individualized study plan.
• Review sample study plans.
• Learn how to register for the exam.

Documentación oficial para el curso Preparing for Your Professional Cloud Security Engineer Journey.

• 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.

Profesionales de la nube o de ingeniería de datos interesados en realizar el examen de certificación Professional Data Engineer.

• Prepararte para el examen de certificación Professional Data Engineer.
• Recibir información, tips y consejos para realizar el examen de certificación.
• A través de la revisión de cada sección del examen, cubrir los conceptos de más alto nivel, desarrollar confianza en tus conocimientos y reconocer las brechas de habilidades o áreas de estudio.

Módulo 1: Understanding the Professional Data Engineer Certification

Temas:

• Position the Professional Data Engineer certification among the offerings
• Distinguish between Associate and Professional
• Provide guidance between Professional Data Engineer and Associate Cloud Engineer
• Describe how the exam is administered and the exam rules
• Provide general advice about taking the exam

Objetivos:

Understand the Professional Data Engineer Certification Exam

Módulo 2: Designing Data Processing Systems

Temas:

• Designing data processing systems
• Designing flexible data representations
• Designing data pipelines
• Designing data processing infrastructure

Objetivos:

Understand the Designing Data Processing Systems component of the Exam

Módulo 3: Building and Operationalizing Data Processing Systems

Temas:

• Building and maintaining data structures and databases
• Building and maintaining flexible data representations
• Building and maintaining pipelines
• Building and maintaining processing infrastructure

Objetivos:

Understand the Building and Operationalizing Data Processing Systems component of the Exam

Módulo 4: Operationalizing Machine Learning Models

Temas:

• Analyzing data and enabling machine learning
• Deploying an ML pipeline
• Machine learning terminology review
• Operationalizing Machine Learning Models: Exam Guide Review
• Modeling business processes for analysis and optimization

Objetivos:

Understand the Operationalizing Machine Learning Models component of the Exam

Módulo 5: Security, Policy, and Reliability

Temas:

• Designing for security and compliance
• Performing quality control
• Ensuring reliability
• Visualizing data and advocating policy
• Ensuring Solution Quality: Exam Guide Review

Objetivos:

Understand the Security, Policy, and Reliability component of the Exam

Módulo 6: Resources and Next Steps

Temas:

• Debrief
• Preparation Resources

Objetivos:

Final Review of the Exam

Documentación oficial para el curso Preparing for Your Professional Cloud Security Engineer Journey.

• 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.

• Analistas, arquitectos e ingenieros de seguridad de la información en la nube.
• Especialistas en seguridad y ciberseguridad de la información.
• Arquitectos de infraestructura en la nube.

• Comprender el enfoque de seguridad de Google.
• Administrar las identidades de administración mediante Cloud Identity.
• Implementar la administración de privilegios mínimos mediante Resource Manager e IAM.
• Implementar el Identity-Aware Proxy.
• Implementar controles de tráfico IP utilizando firewalls VPC y Google Cloud Armor.
• Corregir las vulnerabilidades de seguridad, especialmente el acceso público a datos y máquinas virtuales.
• Buscar y eliminar datos confidenciales con la API de prevención de pérdida de datos en la nube.
• Analizar los cambios en la configuración de los metadatos de los recursos mediante registros de auditoría.
• Escanear una implementación de Google Cloud con Forseti, para solucionar vulnerabilidades importantes, especialmente en el acceso público a los datos y VMs.

Módulo 1: Foundations of Google Cloud Security

• Temas:
  • Google Cloud’s Approach to Security
  • The Shared Security Responsibility Model
  • Threats Mitigated by Google and Google Cloud
  • Access Transparency
• Objetivos:
  • Learn about Google Cloud’s approach to security.
  • Understand the shared security responsibility model.
  • Understand the kinds of threats mitigated by Google and by Google Cloud.
  • Define and understand access transparency.

Módulo 2: Cloud Identity

• Temas:
  • Cloud Identity
  • Google Cloud Directory Sync
  • Google Authentication Versus SAML-based SSO
  • Authentication Best Practices
• Objetivos:
  • Learn what Cloud Identity is and what it does.
  • Learn how Directory Sync securely syncs users and permissions between your on-prem LDAP or AD server and the cloud.
  • Understand the two ways Google Cloud handles authentication and how to set up SSO.
  • Explore best practices for managing groups, permissions, domains and admins with Cloud Identity.

Módulo 3: Identity and Access Management (IAM)

• Temas:
  • Resource Manager
  • IAM Roles
  • IAM Policies
  • IAM Recommender
  • IAM Troubleshooter
  • IAM Audit Logs
  • IAM Best Practices
• Objetivos:
  • Understand Resource Manager: projects, folders, and organizations.
  • Learn how to implement IAM roles, including custom roles.
  • Understand IAM policies, including organization policies.
  • Understand best practices, including separation of duties and least privilege, the use of Google groups in policies, and avoiding the use of basic roles.
  • Learn how to configure IAM, including custom roles and organization policies.

Módulo 4: Configuring Virtual Private Cloud for Isolation and Security

• Temas:
  • VPC Firewalls
  • Load Balancing and SSL Policies
  • Interconnect and Peering Policies
  • Best Practices for VPC Networks
  • VPC Flow Logs
  • Learn best practices for configuring VPC firewalls (both ingress and egress rules).
  • Understand load balancing and SSL policies.
  • Understand how to set up private Google API access.
  • Understand SSL proxy use.
• Objetivos:
  • Learn best practices for VPC networks, including peering and shared VPC use, and the correct use of subnetworks.
  • Learn best security practices for VPNs.
  • Understand security considerations for interconnect and peering options.
  • Become familiar with available security products from partners.
  • Learn to configure VPC firewalls.
  • Prevent data exfiltration with VPC Service Controls.

Módulo 5: Securing Compute Engine: Techniques and Best Practices

• Temas:
  • Service Accounts, IAM Roles and API Scopes
  • Managing VM Logins
  • Organization Policy Controls
  • Compute Engine Best Practices
  • Encrypting Disks with CSEK
• Objetivos:
  • Learn about Compute Engine service accounts, default and customer-defined.
  • Understand IAM roles and scopes for VMs.
  • Understand how Shielded VMs help maintain your system and application integrity.

Módulo 6: Securing Cloud Data: Techniques and Best Practices

• Temas:
  • Cloud Storage IAM permissions and ACLs
  • Auditing Cloud Data
  • Signed URLs and Policy Documents
  • Encrypting with CMEK and CSEK
  • Cloud HSM
  • BigQuery IAM Roles and Authorized Views
  • Storage Best Practices
• Objetivos:
  • Use cloud permissions and roles to secure cloud resources.
  • Audit cloud data.
  • Use signed URLs to give access to objects in a Cloud Storage bucket.
  • Manage what can be placed in a Cloud Storage bucket using Signed Policy Document.
  • Encrypt cloud data using customer managed encryption keys (CMEK), customer supplied encryption keys (CSEK), and Cloud HSM.
  • Protecting data in BigQuery using IAM roles and authorized views.

Módulo 7: Application Security: Techniques and Best Practices

• Temas:
  • Types of Application Security Vulnerabilities
  • Web Security Scanner
  • Threat: Identity and Oauth Phishing
  • Identity-Aware Proxy
  • Secret Manager
• Objetivos:
  • Recall various types of application security vulnerabilities.
  • Understand DoS protections in App Engine and Cloud Functions.
  • Understand the role of Web Security Scanner in mitigating risks.
  • Define and recall the threats posed by Identity and Oauth phishing.
  • Understand the role of Identity-Aware Proxy in mitigating risks.
  • Store application credentials and metadata securely using Secret Manager.

Módulo 8: Securing Google Kubernetes Engine: Techniques and Best Practices

• Temas:
  • Introduction to Kubernetes/GKE
  • Authentication and Authorization
  • Hardening Your Clusters
  • Securing Your Workloads
  • Monitoring and Logging
• Objetivos:
  • Understand the basic components of a Kubernetes environment.
  • Understand how authentication and authorization works in Google Kubernetes Engine.
  • Recall how to harden Kubernetes Clusters against attacks.
  • Recall how to harden Kubernetes workloads against attacks.
  • Understand logging and monitoring options in Google Kubernetes Engine.

Módulo 9: Protecting against Distributed Denial of Service Attacks (DDoS)

• Temas:
  • How DDoS Attacks Work
  • Google Cloud Mitigations
  • Types of Complementary Partner Products
• Objetivos:
  • Understand how DDoS attacks work.
  • Recall common mitigations: Cloud Load Balancing, Cloud CDN, autoscaling, VPC ingress and egress firewalls, Google Cloud Armor.
  • Recall the various types of complementary partner products available.
  • Use Google Cloud Armor to blocklist an IP address and restrict access to an HTTP load balancer.

Módulo 10: Content-Related Vulnerabilities: Techniques and Best Practices

• Temas:
  • Threat Ransomware
  • Ransomware Mitigations
  • Threats: Data Misuse, Privacy Violations, Sensitive Content
  • Content-Related Mitigations
• Objetivos:
  • Discuss the threat of ransomware.
  • Understand ransomware mitigations: Backups, IAM, Cloud Data Loss Prevention API.
  • Understand threats to content: Data misuse, privacy violations, sensitive/restricted/ unacceptable content.
  • Recall mitigations for threats to content: Classifying content using Cloud ML APIs; scanning and redacting data using the DLP API.

Módulo 11: Monitoring, Logging, Auditing, and Scanning

• Temas:
  • Cloud Audit Logs
  • Deploying and Using Forseti
• Objetivos:
  • Understand and use Security Command Center.
  • Understand and use Cloud Monitoring and Cloud Logging.
  • Install the Monitoring and Logging Agents.
  • Understand Cloud Audit Logs.
  • Gain experience configuring and viewing Cloud Audit Logs.
  • Gain experience deploying and using Forseti.
  • Learn how to inventory a deployment with Forseti Inventory.
  • Learn how to scan a deployment with Forseti Scanner.

Documentación oficial para el curso Security in 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.

• Desarrolladores de aplicaciones, arquitectos de soluciones en la nube, ingenieros de DevOps, administradores de TI.
• 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.

Modulo 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

Modulo 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.

Modulo 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

Modulo 4: Introduction to Kubernetes Workloads

• Temas:
  • The kubectl Command
  • Introduction to Deployments
  • Pod Networking
  • Volumes Overview
• Objetivos:
  • Understand the Kubectl command.
  • Understand how Deployments are used in Kubernetes.
  • Understand the networking architecture of Pods.
  • Understand Kubernetes storage abstractions.

Documentación oficial para el curso Getting Started 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.

• Ingeniero de datos.
• Analistas de datos y científicos de datos que aspiren a desarrollar habilidades de ingeniería de datos.

• Demostrar cómo Apache Beam y Dataflow funcionan juntos para satisfacer las necesidades de procesamiento de datos de tu organización.
• Resumir los beneficios de Beam Portability Framework y activarlo para sus canalizaciones de Dataflow.
• Habilitar Shuffle y Streaming Engine, para canalizaciones por lotes y de transmisión respectivamente, para obtener el máximo rendimiento.
• Habilitar Flexible Resource Scheduling para obtener un rendimiento más rentable.
• Seleccionar la combinación correcta de permisos de IAM para el trabajo de Dataflow.
• Implementar las mejores prácticas para un entorno de procesamiento de datos seguro.
• Seleccionar y ajustar I/O de tu elección para la canalización de Dataflow.
• Utilizar esquemas para simplificar tu código Beam y mejorar el rendimiento de su canalización.
• Desarrollar una canalización Beam utilizando SQL y DataFrames.
• Realizar monitoreo, resolución de problemas, pruebas y CI/CD en canalizaciones de Dataflow.

Módulo 1: Introduction

Temas:

• Course Introduction
• Beam and Dataflow Refresher

Objetivos:

• Introduce the course objectives.
• Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs.

Módulo 2: Beam Portability

Temas:

• Beam Portability
• Runner v2
• Container Environments
• Cross-Language Transforms

Objetivos:

• Summarize the benefits of the Beam Portability Framework.
• Customize the data processing environment of your pipeline using custom containers.
• Review use cases for cross-language transformations.
• Enable the Portability framework for your Dataflow pipelines.

Módulo 3: Separating Compute and Storage with Dataflow

Temas:

• Dataflow
• Dataflow Shuffle Service
• Dataflow Streaming Engine
• Flexible Resource Scheduling

Objetivos:

• Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance.
• Enable Flexible Resource Scheduling for more cost-efficient performance.

Módulo 4: IAM, Quotas, and Permissions

Temas:

• IAM
• Quota

Objetivos:

• Select the right combination of IAM permissions for your Dataflow job.
• Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs.

Módulo 5: Security

Temas:

• Data Locality
• Shared VPC
• Private IPs
• CMEK

Objetivos:

• Select your zonal data processing strategy using Dataflow, depending on your data locality needs.
• Implement best practices for a secure data processing environment.

Módulo 6: Beam Concepts Review

Temas:

• Beam Basics
• Utility Transforms
• DoFn Lifecycle

Objetivos:

Review main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading/writing, Utility PTransforms, side inputs), bundles and DoFn Lifecycle.

Módulo 7: Windows, Watermarks, Triggers

Temas:

• Windows
• Watermarks
• Triggers

Objetivos:

• Implement logic to handle your late data.
• Review different types of triggers.
• Review core streaming concepts (unbounded PCollections, windows).

Módulo 8: Sources and Sinks

Temas:

• Sources and Sinks
• Text IO and File IO
• BigQuery IO
• PubSub IO
• Kafka IO
• Bigable IO
• Avro IO
• Splittable DoFn

Objetivos:

• Write the I/O of your choice for your Dataflow pipeline.
• Tune your source/sink transformation for maximum performance.
• Create custom sources and sinks using SDF.

Módulo 9: Schemas

Temas:

• Beam Schemas
• Code Examples

Objetivos:

• Introduce schemas, which give developers a way to express structured data in their Beam pipelines.
• Use schemas to simplify your Beam code and improve the performance of your pipeline.

Módulo 10: State and Timers

Temas:

• State API
• Timer API
• Summary

Objetivos:

• Identify use cases for state and timer API implementations.
• Select the right type of state and timers for your pipeline.

Módulo 11: Best Practices

Temas:

• Schemas
• Handling unprocessable Data
• Error Handling
• AutoValue Code Generator
• JSON Data Handling
• Utilize DoFn Lifecycle
• Pipeline Optimizations

Objetivos:

Implement best practices for Dataflow pipelines.

Módulo 12: Dataflow SQL and DataFrames

Temas:

• Dataflow and Beam SQL
• Windowing in SQL
• Beam DataFrames

Objetivos:

Develop a Beam pipeline using SQL and DataFrames.

Módulo 13: Beam Notebooks

Temas:

Beam Notebooks

Objetivos:

• Prototype your pipeline in Python using Beam notebooks.
• Launch a job to Dataflow from a notebook.

Módulo 14: Monitoring

Temas:

• Job List
• Job Info
• Job Graph
• Job Metrics
• Metrics Explorer

Objetivos:

• Navigate the Dataflow Job Details UI.
• Interpret Job Metrics charts to diagnose pipeline regressions.
• Set alerts on Dataflow jobs using Cloud Monitoring.

Módulo 15: Logging and Error Reporting

Temas:

• Logging
• Error Reporting

Objetivos:

Use the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues.

Módulo 16: Troubleshooting and Debug

Temas:

• Troubleshooting Workflow
• Types of Troubles

Objetivos:

• Use a structured approach to debug your Dataflow pipelines.
• Examine common causes for pipeline failures.

Módulo 17: Performance

Temas:

• Pipeline Design
• Data Shape
• Source, Sinks, and External Systems
• Shuffle and Streaming Engine

Objetivos:

• Understand performance considerations for pipelines.
• Consider how the shape of your data can affect pipeline performance.

Módulo 18: Testing and CI/CD

Temas:

• Testing and CI/CD Overview
• Unit Testing
• Integration Testing
• Artifact Building
• Deployment

Objetivos:

• Testing approaches for your Dataflow pipeline.
• Review frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines.

Módulo 19: Reliability

Temas:

• Introduction to Reliability
• Monitoring
• Geolocation
• Disaster Recovery
• High Availability

Objetivos:

Implement reliability best practices for your Dataflow pipelines.

Módulo 20: Flex Templates

Temas:

• Classic Templates
• Flex Templates
• Using Flex Templates
• Google-provided Templates

Objetivos:

Using flex templates to standardize and reuse Dataflow pipeline code.

Módulo 21: Summary