Buscar resultados para "Kubernetes"|Grupo Loyal

Desde el inicio de la actividad de GRUPO LOYAL en el año 2.005 hasta la actualidad, las líneas de negocio se han visto ampliadas debido a las crecientes necesidades del mercado.

En todas las formaciones realizadas hemos generado una gran satisfacción en las expectativas de nuestros alumnos y clientes.

Cursos impartidos en 2021

Alumnos formados en 2021

Muchas son las razones por las cuales nuestros alumnos nos recomiendan:la atención recibida tanto por el equipo de atención al alumno como por los profesores, la metodología, la calidad de la enseñanza, la cercanía del equipo docente.

Todo ello se ha visto reflejado en un incremento del índice de satisfacción de nuestros alumnos.

Más del 96% de nuestros alumnos nos recomienda a otros

Somos personas colaborativas porque juntos somos más, hacemos más. Sumamos fuerzas para aprender y aportar a nuestros clientes grandes oportunidades. Compartimos conocimiento y experiencias y eso nos convierte en un gran equipo dentro y fuera de GRUPO LOYAL.
Además, con nosotros tendrás la oportunidad de seguir creciendo profesionalmente optando a certificaciones oficiales reconocidas a nivel mundial.

Большие бонусы доступны новичкам на сайте Vavada Казино. Если зарегистрироваться по ссылке, то есть возможность получить приветсвенный бонус 100 фри спинов на лучшие игровые автомамты онлайн. Выигрывайть джекпоты в интернете легко с бонусами Vavada Казино.

Profesores en 2021

GRUPO LOYAL es una compañía puntera y referente en el mundo de la formación certificada y no certificada para organizaciones de primer nivel dentro del sector IT. Me siento realmente encantado colaborando con los magníficos profesionales que forman parte de GRUPO LOYAL. Si buscas un socio de confianza y de excelente rendimiento, GRUPO LOYAL es la solución

José María Berganza

CI (Lean Six Sigma) & H&S Manager - LSSBB/ACPC/SMPC/WITPM-CP

Desde hace bastantes años he impartido formación en áreas técnicas de informática en mi entorno geográfico cercano. GRUPO LOYAL me ha ayudado a aumentar y diversificar mi labor ofreciéndome oportunidades de trabajo en aula virtual que hasta hace poco tiempo no había tenido.

Juan Luis Ochoa

Formador y desarrollador Full Stack

Mi experiencia con Grupo Loyal como formador ha sido de lo más gratificante, aplicando nuevas tecnologías, conociendo a estudiantes de diversos orígenes y culturas, constantemente puedo aprender el arte de brindar saberes y recibir de mis estudiantes, gracias a Grupo Loyal por su lugar en esta relación.

Gino Luciano

DevSecOps Engineer - Certified Information Systems Security Professional

Exámenes realizados en 2021

GRUPO LOYAL gestiona todos los exámenes oficiales del mercado bien en nuestras instalaciones o partners de formación que contamos a nivel nacional.

Google Cloud Fundamentals for Azure Professionals

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Google Cloud Fundamentals for Azure Professionals

This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly.

Objetivos

This course teaches participants the following skills:

Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake
Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more
Manage and monitor applications
Explain feature and pricing model differences

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
5 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This course is intended for the following participants:

Individuals planning to deploy applications and create application environments on Google Cloud Platform.
Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform.
Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs.

Conocimientos requeridos

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

Have basic proficiency with networking technologies like subnets and routing
Have basic proficiency with command-line tools
Have experience with Microsoft Azure and IIS

Temario

Module 1: Introducing Google Cloud

Explain the advantages of Google Cloud.
Define the components of Google’s network infrastructure, including: Points of presence, data centers, regions, and zones.
Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS).
Module 2: Getting Started with Google Cloud

Identify the purpose of projects on Google Cloud.
Understand how Azure’s resource hierarchy differs from Google Cloud’s.
Understand the purpose of and use cases for Identity and Access Management.
Understand how Azure AD differs from Google Cloud IAM.
List the methods of interacting with Google Cloud.
Launch a solution using Cloud Marketplace.
Module 3: Virtual Machines in the Cloud

Identify the purpose and use cases for Google Compute Engine.
Understand the basics of networking in Google Cloud.
Understand how Azure VPC differs from Google VPC.
Understand the similarities and differences between Azure VM and Google Compute Engine.
Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure.
Deploy applications using Google Compute Engine.
Module 4: Storage in the Cloud

Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore.
Understand how Azure Blob compares to Cloud Storage.
Compare Google Cloud’s managed database services with Azure SQL.
Learn how to choose among the various storage options on Google Cloud.
Load data from Cloud Storage into BigQuery.
Module 5: Containers in the Cloud

Define the concept of a container and identify uses for containers.
Identify the purpose of and use cases for Google Container Engine and Kubernetes.
Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine.
Provision a Kubernetes cluster using Kubernetes Engine.
Deploy and manage Docker containers using kubectl.
Module 6: Applications in the Cloud

Understand the purpose of and use cases for Google App Engine.
Contrast the App Engine Standard environment with the App Engine Flexible environment.
Understand how App Engine differs from Azure App Service.
Understand the purpose of and use cases for Google Cloud Endpoints.
Module 7: Developing, Deploying and Monitoring in the Cloud

Understand options for software developers to host their source code.
Understand the purpose of template-based creation and management of resources.
Understand how Google Cloud Deployment Manager differs from Azure Resource Manager.
Understand the purpose of integrated monitoring, alerting, and debugging.
Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics.
Create a Deployment Manager deployment.
Update a Deployment Manager deployment.
View the load on a VM instance using Google Monitoring.
Module 8: Big Data and Machine Learning in the Cloud

Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.
Understand how Google Cloud BigQuery differs from Azure Data Lake.
Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus.
Understand how Google Cloud’s machine-learning APIs differ from Azure’s.
Load data into BigQuery from Cloud Storage.
Perform queries using BigQuery to gain insight into data.
Module 9: Summary and Review

Review the products that make up Google Cloud and remember how to choose among them.
Understand next steps for training and certification.
Understand, at a high level, the process of migrating from Azure to Google Cloud.

Solicita información del curso

Google Cloud Fundamentals: Core Infrastructure

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Google Cloud Fundamentals: Core Infrastructure

Con esta sesión de un día, adquirirás una visión general de los productos y servicios de Google Cloud. A través de una combinación de presentaciones, demostraciones y laboratorios prácticos, los participantes aprenden el valor de Google Cloud y cómo incorporar a sus estrategias de negocio las soluciones basadas Google Cloud.

Objetivos

Este curso te prepara para aspectos como:

Identificar el propósito y el valor de los productos y servicios de la plataforma de Google Cloud
Interactuar con los servicios de la plataforma Google Cloud
Describir las formas en que los clientes han utilizado la plataforma de la nube de Google
Elegir y utilizar los entornos de despliegue de aplicaciones de la plataforma Google Cloud: Google App Engine, Google Kubernetes Engine y Google Compute Engine
Aplicar las opciones de almacenamiento de la plataforma Google Cloud: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable y Google Cloud Datastore
Hacer un uso básico de BigQuery, el almacén de datos gestionado de Google para el análisis
Hacer uso básico de Cloud Deployment Manager, la herramienta de Google para crear y administrar recursos de la nube a través de plantillas
Hacer uso básico de Google Stackdriver, el sistema de monitoreo, registro y diagnóstico de Google

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
5 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

Esta sesión está orientada a perfiles como:

Profesionales que planean desplegar aplicaciones y construir entornos de aplicaciones en la plataforma de la nube de Google.
Desarrolladores, profesionales de operaciones de sistemas y arquitectos de soluciones que empiezan a trabajar con la plataforma Google Cloud.
Ejecutivos y responsables de la toma de decisiones empresariales que están evaluando el potencial de la plataforma Google Cloud para satisfacer sus necesidades empresariales.

Conocimientos requeridos

Familiaridad con la línea de comandos de Linux, servidores web y editores de texto

Temario

Módulo 1: Presentación de la plataforma de la nube de Google

Explica las ventajas de la plataforma de la nube de Google.

Definir los componentes de la infraestructura de la red de Google, incluyendo: Puntos de presencia, centros de datos, regiones y zonas.

Comprender la diferencia entre la infraestructura como servicio (IaaS) y la plataforma como servicio (PaaS)

Módulo 2: Introducción a la plataforma Google Cloud

Identificar el propósito de los proyectos en la plataforma de la nube de Google.

Comprender el propósito y los casos de uso de la gestión de identidad y acceso.

Enumerar los métodos de interacción con la plataforma Google Cloud.

Laboratorio: Introducción a la plataforma Google Cloud.

Módulo 3: Máquinas virtuales y redes en la nube

Identificar el propósito y los casos de uso de Google Compute Engine.

Comprender las diversas herramientas y servicios operativos y de red de la plataforma de Google Cloud.

Laboratorio: Motor de Computación

Módulo 4: Almacenamiento en la nube

Comprender el propósito de los casos y utilizarlos para..: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable y Google Cloud Datastore.

Aprende a elegir entre las diferentes opciones de almacenamiento en la plataforma Google Cloud.

Laboratorio: Almacenamiento en la nube y SQL en la nube

Módulo 5: Contenedores de nubes

Definir el concepto de contenedor e identificar los usos de los contenedores.

Identificar el propósito y los casos de uso del Motor de Kubernetes de Google y Kubernetes.

Laboratorio: Motor de kubernetes

Módulo 6: Aplicaciones de la nube

Comprender el propósito y los casos de uso de Google App Engine.

Contrasta el entorno estándar de App Engine con el entorno flexible de App Engine.

Comprender el propósito y los casos de uso de Google Cloud Endpoints.

Laboratorio: Motor de aplicaciones

Módulo 7: Desarrollo, implementación y monitoreo en la nube

Comprender las opciones que tienen los desarrolladores de software para alojar su código fuente.

Comprender el propósito de la creación y gestión de recursos basados en plantillas

Comprender el propósito de la vigilancia, la alerta y la depuración integradas.

Laboratorio: Administrador de Despliegue y Stackdriver

Módulo 8: Grandes datos y aprendizaje de máquinas en la nube

Comprender el propósito y los casos de uso de productos y servicios en grandes plataformas de aprendizaje de datos y máquinas de Google Cloud.

Laboratorio: BigQuery

Solicita información del curso

Google Cloud Fundamentals: Core Infrastructure

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Google Cloud Fundamentals: Core Infrastructure

This one-day instructor-led class provides an overview of Google Cloud products and services. Through a combination of presentations, demonstrations and hands-on labs, participants learn the value of the Google Cloud and how to incorporate cloud-based solutions into business strategies.

Objetivos

This course teaches participants the following skills:

Identify the purpose and value of Google Cloud products and services.
Interact with Google Cloud services
Describe the ways in which customers have used the Google Cloud
Choose from and use Google Cloud Platform application deployment environments: Google App Engine, Google Kubernetes Engine, and Google Compute Engine
Choose from and use Google Cloud storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable and Google Cloud Datastore
Make basic use of BigQuery, Google’s managed data warehouse for analysis.
Make basic use of Cloud Deployment Manager, Google’s tool for creating and managing cloud resources through templates.
Make basic use of Google Stackdriver, Google’s monitoring, logging and diagnostic system.

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
5 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This class is intended for the following profiles:

People who plan to deploy applications and build application environments on the Google Cloud .
Developers, system operations professionals, and solution architects who are starting to work with the Google Cloud .
Executives and business decision makers who are evaluating the potential of the Google Cloud to meet their business needs.

Conocimientos requeridos

Familiarity with the Linux command line, web servers and text editors

Temario

Module 1: Introducing the Google Cloud

Explain the advantages of the Google Cloud .
Define the components of Google’s network infrastructure, including: Points of Presence, data centers, regions and zones.
Understand the difference between infrastructure as a service (IaaS) and platform as a service (PaaS).
Module 2: Introduction to the Google Cloud

Identify the purpose of projects on the Google Cloud .
Understand the purpose and use cases of identity and access management.
List the methods of interaction with the Google Cloud .
Lab: Introduction to the Google Cloud .

Module 3: Virtual Machines and Cloud Networks

Identify the purpose and use cases of Google Compute Engine.
Understand the various operational and network tools and services of the Google Cloud .
Lab: Compute Engine

Module 4: Cloud Storage

Understand the purpose of cases and use them to: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable and Google Cloud Datastore.
Learn how to choose between the different storage options in the Google Cloud .
Lab: Cloud storage and cloud SQL
Module 5: Cloud Containers

Define the concept of container and identify the uses of containers.
Identify the purpose and use cases of Google Kubernetes Engine and Kubernetes.
Lab: Kubernetes Engine
Module 6: Cloud Applications

Understand the purpose and use cases of Google App Engine.
Contrast the Standard App Engine environment with the Flexible App Engine environment.
Understand the purpose and use cases of Google Cloud Endpoints.
Lab: Application engine
Module 7: Development, Implementation and Monitoring in the Cloud

Understand the options software developers have for hosting their source code.
Understand the purpose of template-based resource creation and management
Understand the purpose of integrated monitoring, alerting and debugging.
Lab: Deployment Manager and Stackdriver
Module 8: Great data and machine learning in the cloud

Understand the purpose and use cases of products and services on large data learning platforms and Google Cloud machines.
Lab: BigQuery

Solicita información del curso

Implementing Cisco HyperFlex

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Implementing Cisco HyperFlex

The Implementing Cisco HyperFlex course shows you how to deploy and use the Cisco HyperFlex data platform to support multicloud workloads. You will become familiar with HyperFlex components and learn how to install, design, and manage HyperFlex to support highly scalable and resilient multicloud implementations. You will also gain hands-on experience focused on installation, management, and native replication, you will explore cluster technologies as well as Cisco Intersight.™

Objetivos

After completing this course you should be able to:

Describe hyperconvergence, Cisco HyperFlex, and the components of Cisco HyperFlex
Explain the Cisco Unified Computing System™ (Cisco UCS®) and what makes it valuable to business
Describe how Cisco HyperFlex Data Platform (HXDP) and HX maintenance mode work
Describe the physical components of Cisco HyperFlex
Install and expand Regular vSphere Cisco HyperFlex
Manage your Cisco HyperFlex VMware ESXi-based cluster
Describe how to maintain Cisco HyperFlex
Design a Cisco HyperFlex solution and explain third-party data restore solutions
Protect the data on your Cisco HyperFlex cluster using replication and data at rest encryption
Describe a stretched cluster and how is it different from a standard cluster
Describe an Edge cluster and how is it different from a standard cluster
Describe a HyperV-based cluster and how is it different from an ESXi-based cluster
Design a multicloud datacenter with Cisco HyperFlex
Perform basic troubleshooting tasks and explain Cisco Intersight
Explain Cisco HyperFlex releases beyond 3.5(1a)

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
15 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

Data Center engineers involved in the implementation of a Hyperflex multicloud solution.

Conocimientos requeridos

Attendees should meet the following prerequisites:

Cisco CCNA level knowledge about data center architecture and products technologies (network, compute, storage network) – DCICN, DCICT and DCUCI recommended
Familiarity with VMware vCenter and ESXi – VSICM recommended
Familiarity with Microsoft Server 2016 and Hyper-V –
Pre-requisitos:
CCNA – Implementing and Administering Cisco Solutions
DCFNDU – Understanding Cisco Data Center Foundations
VSICM – VMware vSphere: Install, Configure, Manage

Temario

Introducing Hyperconvergence and Cisco HyperFlex

Traditional Data Center Design
What Is Hyperconvergence?
What Is Cisco HyperFlex?
Cisco HyperFlex Primer
Evolution of Cisco HyperFlex
Describing Cisco UCS: The Foundation of Cisco HyperFlex

Cisco Server Deployment Models: Standalone Versus Managed
Cisco UCS Managed Model Benefits
Cisco UCS M5 Overview
Cisco UCS M5 Server Types
Cisco Virtual Interface Cards (VICs) and Their Benefits
Cisco UCS Fabric Interconnects
Cisco UCS Manager
Describing Cisco HyperFlex Software Components

Virtual Machine Hypervisor
Log-Structured File System
Cisco HyperFlex Snapshots Versus VMware Snapshots
Cisco HyperFlex Versus Regular Virtualized Server
Cisco HyperFlex Data Distribution
Writing and Reading Process
Data Optimization Overview
Cisco HyperFlex vs. Other Hyper-Converged Infrastructure (HCI) Solutions
Describing Cisco HyperFlex Hardware Components

HX UCS M4 and M5
Introducing Cisco HyperFlex Servers
Storage Technologies in Cisco HyperFlex
Storage Components of Cisco HyperFlex Converged Nodes
Non-Storage Components of Cisco HyperFlex Converged Nodes
Cisco UCS Fabric Interconnects
Compute-Only Nodes
Installing and Expanding Standard ESXi Cisco HyperFlex

Installation Summary
Software Prerequisites
Hardware Prerequisites
Cisco HyperFlex Networking
Required Deployment Information
vCenter for HyperFlex
Installing Physical Components
Configure Upstream Switches
Prepare Fabric Interconnects
Deploy the Installer Virtual Machine (VM)
HyperFlex Installation
Post-Installation Script
Cluster Expansion
Additional Installation Options
Managing Cisco HyperFlex in vSphere Environment

Management Interfaces Overview
Cisco HyperFlex Plugin for vCenter
Cisco HyperFlex Connect
Storage Command Line Interface
Representational State Transfer (REST) API Overview
ReadyClones
Cisco HyperFlex Snapshots
Maintaining Cisco HyperFlex

Installer Custom Workflow: Use Cases
Cisco HyperFlex Upgrade Overview
Cisco HyperFlex Online Upgrade
Cisco HyperFlex Offline Upgrade
HX Maintenance Mode
ESXi Upgrade
Moving Cisco Hyperflex Storage Cluster to Another vCenter
Designing Cisco HyperFlex

Cluster Resiliency: VM-Level
Cluster Resiliency: HXDP-Level
Cisco HyperFlex Cluster Scalability
Cluster Capacity
Multiple Clusters on One Cisco UCS Domain
Cisco HyperFlex and External Storages
Licensing Tiers
Smart Licensing
Cisco HyperFlex Positioning
Graphical Processing Units and HyperFlex
Protecting Your Data

Disaster Recovery Overview
Third-Party Data Restore Solutions
Cisco HyperFlex Native Replication Solution
Configuring Native Replication
Protecting Your VMs with Native Replication
Restoring Your VMs with Native Replication
Data at Rest Encryption
D@RE: Remote Key Management
Introducing Stretched Cluster

Stretched Cluster Overview
Prerequisites
Data Distribution
Datastores and VM Affinity
Installation Process
Maintenance and Monitoring
Introducing EDGE Cluster

Cisco Hyperflex EDGE Cluster Overview
Prerequisites and Recommendations
Installation Process
Management and Monitoring
Upgrades and Maintenance
Introducing HyperV-Based Regular Cluster

HyperV-Based Standard Cluster Overview
Prerequisites and Recommendations
Preinstallation Tasks
Installation Process
Post-Installation Process
Maintenance and Monitoring
Designing Multicloud Data Center with Cisco HyperFlex

Cisco UCS Director Overview
Cisco CloudCenter Overview
Cisco Workload Optimization Manager (CWOM) Overview
Cisco HyperFlex with Kubernetes and Cisco Container Platform
Cisco Intersight Overview
HyperFlex: Releases Beyond 3.5(1a)
Labs

Lab 1: Investigate Software Components of HyperFlex
Lab 2: Investigate Cisco UCS Part of HyperFlex
Lab 3: Install Cisco HyperFlex
Lab 4: Manage Cisco HyperFlex
Lab 5: Protect Your HyperFlex VMs
Lab 6: Install and Manage Stretched Cluster

Solicita información del curso

Kubernetes Application Essentials

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Kubernetes Application Essentials

Whether your focus is on developing containerized applications or managing a container-centric infrastructure, CN120 introduces Kubernetes container orchestration for everyone involved in the software development life cycle. Through a real-world approach for design and deployment considerations, students can expect to learn about the foundational Kubernetes components required for application workloads. Specifically, students will examine Kubernetes architecture, explore how Kubernetes objects work together for running an application, and learn how Kubernetes makes use of compute, networking, and storage resources.

Objetivos

Make effective use of pod architecture
Deploy workloads as Kubernetes controllers
Provision configuration at runtime to Kubernetes workloads
Network pods together across a cluster using native services
Provision highly available storage to Kubernetes workloads
Package an application as a Helm chart

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
10 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This course is targeted at students with the following:

Motivations: General technical audiences & IT professionals

Roles: Architect basic applications for Kubernetes

Conocimientos requeridos

Temario

Lab Requirements
Laptop with WiFi connectivity
Attendees should have the latest Chrome or Firefox installed, and a free account at strigo.io.

Solicita información del curso

Kubernetes Native Application Development

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Kubernetes Native Application Development

In this Cloud Native course, developers will learn how to build containerized applications targeted for enterprise-grade production environments. You’ll explore patterns in containerized application architecture, techniques for eliminating friction in the development process, how to test and debug containerized applications, and how to instrument applications with healthchecks, monitoring tools, and common container logging patterns. After mastering these techniques, we’ll turn our attention to devops and building container-native continuous integration pipelines powered by Jenkins and Kubernetes.

Objetivos

Rapid code-build-test iteration cycles
Developer-local Kubernetes environments
IDE & debugger container integration
Optimizing container startup
Gracefully handling container failure
Containerizing legacy applications
Instrumenting containers with health checks
Log management strategies
Instrumenting containers with Prometheus monitoring
Unit and integration tests for containerized applications
Containerized continuous integration pipelines
Image and config hierarchies in CI
Build server management
Unit and integration testing pipelines
Integrating Jenkins with registries, Kubernetes clusters and standalone Docker engines
Securing and packaging applications for production

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
10 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This course is targeted at students with the following:

Motivations: Develop container-native applications, and implement fully containerized CI

Roles: Developers, application architects, devops

Conocimientos requeridos

Familiarity with the Bash shell
Filesystem navigation and manipulation
Command line text editors like vim or nano
Common tooling like curl, wget and ping
Familiarity with YAML and JSON notation
Basic familiarity with common software development patterns and tools like version control, testing, continuous integration and logging.
Pre-requisitos:
CN120 – Kubernetes Application Essentials

Temario

Container Development Environments
The Container Lifecycle
Containerizing Applications
Container health and Monitoring
Introduction to containerized CI
CI Agent Deployment
Building Images in CI
Testing in CI
Releasing Containerized Applications

Solicita información del curso

Big Data and Machine Learning Fundamentals

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Big Data and Machine Learning Fundamentals

Big Data and Machine Learning Fundamentals es un curso de un día dictado por un instructor que presenta a los participantes las capacidades de macrodatos de Google Cloud.

Mediante una combinación de presentaciones, demostraciones y labs prácticos, los participantes obtendrán una descripción general de Google Cloud, además de información detallada sobre el procesamiento de datos y las capacidades del aprendizaje automático.

Objetivos

Al finalizar esta formación los participantes serán capaces de:

Identificar el objetivo y el valor de los productos y servicios de Google Cloud
Interactuar con los servicios de Google Cloud
Describir las formas en que los clientes han usado Google Cloud
Usar productos de macrodatos y AA en Google Cloud: App Engine, Google Kubernetes Engine y Compute Engine
Usar BigQuery, el almacén de datos administrado de Google para las estadísticas

data

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
8 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

Conocimientos requeridos

Temario

Módulo 1: Introducción a Google Cloud

Identifica los diferentes aspectos de la infraestructura de Google Cloud.
Identifique los productos de Big Data y ML que forman Google Cloud.
Módulo 2: Recomendar productos con Cloud SQL y Spark

Revise cómo las empresas utilizan los modelos de recomendación.
Evalúe cómo y dónde calculará y almacenará los resultados de su modelo de alquiler de vivienda.
Analice cómo la ejecución de Hadoop en la nube con Dataproc puede permitir la escala.
Evaluar diferentes enfoques para almacenar datos de recomendaciones fuera del clúster.
Módulo 3: Predecir las compras de visitantes con BigQuery ML

Analice macrodatos a escala con BigQuery.
Descubra cómo BigQuery procesa las consultas y almacena datos a gran escala.
Tutoriales de términos clave de AA: características, etiquetas, datos de entrenamiento.
Evalúe los diferentes tipos de modelos para conjuntos de datos estructurados.
Crea modelos de AA personalizados con BigQuery ML.
Módulo 4: Paneles de control en tiempo real con Pub / Sub, Dataflow y Google Data Studio

Identifique los desafíos de la canalización de datos modernos y cómo resolverlos a escala con Dataflow.
Diseñe canalizaciones de transmisión con Apache Beam.
Cree paneles colaborativos en tiempo real con Data Studio.
Módulo 5: Obtención de conocimientos a partir de datos no estructurados mediante el aprendizaje automático

Evalúe cómo las empresas utilizan modelos de AA no estructurados y cómo funcionan los modelos.
Elija el enfoque correcto para los modelos de aprendizaje automático entre predefinidos y personalizados.
Cree un modelo de clasificación de imágenes personalizado de alto rendimiento sin código con AutoML.
Módulo 6: Resumen

Resumen de los puntos clave de aprendizaje.
Recursos

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Kubernetes Operations

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Kubernetes Operations

This class centers on developing the skills and knowledge needed for Day-1 Kubernetes operations for managing applications. Using best practices as guiding principles, students will engage in topics pertaining to the Kubernetes architecture to make informed decisions for production workloads. Topics include configuring resource availability for applications, implementing advanced scheduling for applications, and administering user roles and permissions for the Kubernetes cluster. Kubernetes Operations and System Integration teams will benefit greatly as they plan and deploy their Kubernetes production environments.

Objetivos

Kubernetes Architecture
Managing Application Security
Releasing updates to Kubernetes workloads
Scheduling options for Kubernetes workloads
Managing Kubernetes cluster resources
Mapping ingress to an application
Enabling security in the Kubernetes Security Model
Provisioning networking within the Kubernetes cluster
Implementing multi-tenancy in a Kubernetes cluster

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
10 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This course is targeted at students with the following:

Motivations: Perform core ‘day-1’ operational tasks on Kubernetes

Roles: Infrastructure and Integration engineers tasked with day-to-day application workload management; IT professionals looking to expand skills in Kubernetes administration

Conocimientos requeridos

Temario

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Logging, Monitoring, and Observability in Google

It Formacion — Wed, 01 Dec 2021 09:20:00 +0000

Logging, Monitoring, and Observability in Google

Logging, Monitoring and Observability in Google Cloud teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.

Objetivos

This course teaches participants the following skills:

Plan and implement a well-architected logging and monitoring infrastructure
Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs)
Create effective monitoring dashboards and alerts
Monitor, troubleshoot, and improve Google Cloud infrastructure
Analyze and export Google Cloud audit logs
Find production code defects, identify bottlenecks, and improve performance
Optimize monitoring costs

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Acceso al campus

Subvención disponible
A través de Fundae, cumpliendo requisitos.

Duración
15 horas

Dificultad 50% 50%
Nivel alcanzado 80% 80%

Dirigido a

This class is intended for the following participants:

Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel.

Conocimientos requeridos

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

Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience
Basic scripting or coding familiarity
Proficiency with command-line tools and Linux operating system environments

Temario

Module 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.
Module 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.
Module 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.
Module 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.
Module 5: Configuring Google Cloud Services for Observability

Integrate logging and monitoring agents into Compute Engine VMs and images.
Enable and utilize Kubernetes Monitoring.
Extend and clarify Kubernetes monitoring with Prometheus.
Expose custom metrics through code, and with the help of OpenCensus.
Module 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.
Link application errors to Logging using Error Reporting.
Export logs to BigQuery.
Module 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.
Module 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.
Module 9: Investigating Application Performance Issues

Debug production code to correct code defects.
Trace latency through layers of service interaction to eliminate performance bottlenecks.
Profile and identify resource-intensive functions in an application.
Module 10: Optimizing the Costs of Monitoring

Analyze resource utilization cost for monitoring related components within Google Cloud.
Implement best practices for controlling the cost of monitoring within Google Cloud.

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