Fundación Tripartita
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Kubernetes Master Class

Registro para Kubernetes Master Class

Objetivos

Este curso enseña a los asistentes a:

  • Introducción a los componentes básicos de Kubernetes: Pods, Services, Deployments, Replica Sets, Ingress.
  • Rolling updates y rollbacks.
  • Buenas prácticas con Kubernetes: tamaño de la imagen del contenedor, límite de recursos, resource limits, liveness and readiness probes, pod/node affinity and antiaffinity, taints and tolerations.
  • Autoscaling, Autoupdates y Autorepair en Google Kubernetes Engine. Clusters multizona.
  • Pipelines de CI/CD con Kubernetes y Helm.
  • Monitorización y alertas con StackDriver.
  • Actualizaciones de las últimas tres versiones de Kubernetes: 1.6, 1.7, 1.8.

Contenido

  1. Intro to Kubernetes
    • Pods, Nodes, Replica Sets, Deployments, Services, Persistent Volumes
    • Rolling updtaes and deployment rollback
    • Web console for Kubernetes and Kubernetes Engine
    • Stateful workloads in Kubernetes
  2. Best Practices
    • ConfigMaps and Secrets
    • Reserving Resources
    • Liveness and Readiness Probes
    • Static IP Addresses
    • Upgrading Kubernetes
    • Rolling out new container images
  3. Updates in Kubernetes 1.6/1.7/1.8
    • Scheduling pods into nodes
    • Storage
    • Networking
    • Security
  4. CI/CD with Kubernetes
    • Adding containers to your CI/CD pipeline
    • Kubernetes with Jenkins
    • Kubernetes with Container Builder

Deep Learning for Text

Registro para Deep Learning for Text

Objetivos

Este curso enseña a los asistentes a:

  • Crear modelos para clasificar textos.
  • Etiquetado de palabras.
  • Construir modelos de lenguaje usando técnicas de deep learning con tus propios conjuntos de datos.

Contenido

  1. Intro to deep learning
    • Overview
  2. Intro to deep learning with text data
    • Intro to the recurrent neural networks. Basic and advanced models
    • Development of models with deep recurrent neural networks: Network parametrization, training strategies, regularization and embeddings
  3. Intro to the working environment
    • GCP GPU machines
    • Jupyter notebook
    • TensorFlow
  4. Use cases
    • Use case 1: Classify texts into categories. Build a sentiment model from scratch
    • Use case 2: word labelling
    • Use case 3: Language models. Build a character level and a word level language models

Deep Learning for Images

Registro para Deep Learning for Images

Objetivos

Este curso enseña a los asistentes a:

  • Crear modelos para clasificar imágenes.
  • Detectar objetos.
  • Construir modelos usando técnicas de deep learning con tus propios conjuntos de datos.

Contenido

  1. Intro to deep learning
    • Overview
  2. Intro to deep learning applied to images
    • Understand image data
    • Convolutional neural networks
  3. Intro to the working environment
    • GCP GPU machines
    • Jupyter notebook
    • TensorFlow
  4. Use case 1: Classify images from scratch
    • Development of the models: Net parametrization, regularization and visualization of net and results
    • Data augmentation strategies
  5. Use case 2: Classify images with pre-builded models - Transfer learning
    • Understand the most relevant network architectures for image data
  6. Use case 3: Object detection in images. A first approach
    • Develop of a first model to object detection in images

Google Cloud Fundamentals: Core Infrastructure

Registro para Google Cloud Fundamentals: Core Infrastructure

Objetivos

Este curso enseña a los asistentes a:

  • Identificar el propósito y el valor de los productos y servicios de Google Cloud
  • Interactuar con los servicios de Google Cloud
  • Describir formas en que los clientes han utilizado Google Cloud
  • Elegir un entorno de despliegue adecuando en Google Cloud: Google App Engine, Google Kubernetes Engine o Google Compute Engine
  • Desplegar una aplicación en Google App Engine, Google Kubernetes Engine y Google Compute Engine
  • Usar y elegir entre las diferentes opciones de almacenamiento de Google Cloud: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable y Google Cloud Datastore
  • Hacer un uso básico de BigQuery, el warehouse de datos administrado por Google para el análisis de datos
  • Hacer un uso básico de Cloud Deployment Manager, la herramienta de Google para crear y administrar recursos en la nube mediante plantillas
  • Hacer un uso básico de Google Stackdriver, el sistemas de monitorización, logging y diagnósticos de Google

Contenido

  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)
  2. Getting Started with Google Cloud
    • Identify the purpose of projects on Google Cloud
    • Understand the purpose of and use cases for Identity and Access Management
    • List the methods of interacting with Google Cloud
    • Lab: Getting Started with Google Cloud
  3. Virtual Machines and Networks in the Cloud
    • Identify the purpose of and use cases for Google Compute Engine
    • Understand the various Google Cloud Platform networking and operational tools and services
    • Lab: Compute Engine
  4. Storage in the Cloud
    • Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore
    • Learn how to choose between the various storage options on Google Cloud
    • Lab: Google Cloud Storage and Cloud SQL
  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 Kubernetes Engine and Kubernetes
    • Lab: Kubernetes Engine
  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 the purpose of and use cases for Google Cloud Endpoints
    • Lab: App Engine
  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 the purpose of integrated monitoring, alerting, and debugging
    • Lab: Deployment Manager and Stackdriver
  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
    • Lab: BigQuery

From Data to Insights with Google Cloud

Registro para From Data to Insights with Google Cloud

Objetivos

Este curso enseña a los asistentes a:

  • Extraer información de datos usando las herramientas de análisis y visualización de Google Cloud
  • Consultar datasets de forma interactiva usando Google BigQuery
  • Cargar, limpiar y transformar datos a gran escala
  • Visualizar datos usando Google Data Studio y otras plataformas de terceros
  • Distinguir entre exploratory y explanatory analytics y cuándo usar cada uno
  • Optimizar el precio y el rendimiento del modelo de datos y las consultas

Contenido

  1. Introduction to Data on the Google Cloud
    • Highlight Analytics Challenges Faced by Data Analysts
    • Compare Big Data On-Premise vs on the Cloud
    • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
    • Navigate Google Cloud Project Basics
    • Lab: Getting started with Google Cloud
  2. Big Data Tools Overview
    • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Data Tools
    • Demo: Analyze 10 Billion Records with Google BigQuery
    • Explore 9 Fundamental Google BigQuery Features
    • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
    • Lab: Exploring Datasets with Google BigQuery
  3. Exploring your Data with SQL
    • Compare Common Data Exploration Techniques
    • Learn How to Code High Quality Standard SQL
    • Explore Google BigQuery Public Datasets
    • Visualization Preview: Google Data Studio
    • Lab: Troubleshoot Common SQL Errors
  4. Google BigQuery Pricing
    • Walkthrough of a BigQuery Job
    • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
    • Optimize Queries for Cost
    • Lab: Calculate Google BigQuery Pricing
  5. Cleaning and Transforming your Data
    • Examine the 5 Principles of Dataset Integrity
    • Characterize Dataset Shape and Skew
    • Clean and Transform Data using SQL
    • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
    • Lab: Explore and Shape Data with Cloud Dataprep
  6. Storing and Exporting Data
    • Compare Permanent vs Temporary Tables
    • Save and Export Query Results
    • Performance Preview: Query Cache
    • Lab: Creating new Permanent Tables
  7. Ingesting New Datasets into Google BigQuery
    • Query from External Data Sources
    • Avoid Data Ingesting Pitfalls
    • Ingest New Data into Permanent Tables
    • Discuss Streaming Inserts
    • Lab: Ingesting and Querying New Datasets
  8. Data Visualization
    • Overview of Data Visualization Principles
    • Exploratory vs Explanatory Analysis Approaches
    • Demo: Google Data Studio UI
    • Connect Google Data Studio to Google BigQuery
    • Lab: Exploring a Dataset in Google Data Studio
  9. Joining and Merging Datasets
    • Merge Historical Data Tables with UNION
    • Introduce Table Wildcards for Easy Merges
    • Review Data Schemas: Linking Data Across Multiple Tables
    • Walkthrough JOIN Examples and Pitfalls
    • Lab: Join and Union Data from Multiple Tables
  10. Advanced Functions and Clauses
    • Review SQL Case Statements
    • Introduce Analytical Window Functions
    • Safeguard Data with One-Way Field Encryption
    • Discuss Effective Sub-query and CTE design
    • Compare SQL and Javascript UDFs
    • Lab: Deriving Insights with Advanced SQL Functions
  11. Schema Design and Nested Data Structures
    • Compare Google BigQuery vs Traditional RDBMS Data Architecture
    • Normalization vs Denormalization: Performance Tradeoffs
    • Schema Review: The Good, The Bad, and The Ugly
    • Arrays and Nested Data in Google BigQuery
    • Lab: Querying Nested and Repeated Data
  12. More Visualization with Google Data Studio
    • Create Case Statements and Calculated Fields
    • Avoid Performance Pitfalls with Cache considerations
    • Share Dashboards and Discuss Data Access considerations
  13. Optimizing for Performance
    • Avoid Google BigQuery Performance Pitfalls
    • Prevent Hotspots in your Data
    • Diagnose Performance Issues with the Query Explanation map
    • Lab: Optimizing and Troubleshooting Query Performance
  14. Advanced Insights
    • Introducing Cloud Datalab
    • Cloud Datalab Notebooks and Cells
    • Benefits of Cloud Datalab
  15. Data Access
    • Compare IAM and BigQuery Dataset Roles
    • Avoid Access Pitfalls
    • Review Members, Roles, Organizations, Account Administration, and Service Accounts

Google Cloud Fundamentals: Big Data & Machine Learning

Registro para Google Cloud Fundamentals: Big Data & Machine Learning

Objetivos

Este curso enseña a los asistentes a:

  • Identificar el propósito y el valor de los productos de Big Data y Machine Learning ofrecidos en Google Cloud
  • Usar CloudSQL y Cloud Dataproc para migrar datos de MySQL y Hadoop/Pig/Spark/Hive a Google Cloud
  • Utilizar BigQuery y Cloud Datalab para realizar un análisis de datos interactivo
  • Usar una red neuronal utilizando TensorFlow
  • Utilizar las APIs de ML
  • Elegir entre los diferentes productos de procesamiento de datos de Google Cloud

Contenido

  1. Introducing Google Cloud
    • Google Platform Fundamentals Overview
    • Google Cloud Platform Big Data Products
  2. Compute and Storage Fundamentals
    • CPUs on demand (Compute Engine)
    • A global filesystem (Cloud Storage)
    • CloudShell
    • Lab: Set up a Ingest-Transform-Publish data processing pipeline
  3. Data Analytics on the Cloud
    • Stepping-stones to the cloud
    • CloudSQL: your SQL database on the cloud
    • Lab: Importing data into CloudSQL and running queries
    • Spark on Dataproc
    • Lab: Machine Learning Recommendations with SparkML
  4. Scaling Data Analysis
    • Fast random access
    • Datalab
    • BigQuery
    • Lab: Build machine learning dataset
  5. Machine Learning
    • Machine Learning with TensorFlow
    • Lab: Carry out ML with TensorFlow
    • Pre-built models for common needs
    • Lab: Employ ML APIs
  6. Data Processing Architectures
    • Message-oriented architectures with Pub/Sub
    • Creating pipelines with Dataflow
    • Reference architecture for real-time and batch data processing
  7. Summary
    • Why GCP?
    • Where to go from here
    • Additional Resources

Data Engineering on Google Cloud

Registro para Data Engineering on Google Cloud

Objetivos

Este curso enseña a los asistentes a:

  • Diseñar y construir sistemas de procesamiento de datos en Google Cloud
  • Procesamiento de datos en batch y streaming implementando pipelines de datos autoescalables sobre Cloud Dataflow
  • Obtener información de negocio de conjuntos de datos extremadamente grandes usando Google BigQuery
  • Entrenar, evaluar y predecir usando modelos de machine learning usando TensorFlow y Cloud ML
  • Aprovechar los datos no estructurados con las APIs de Spark y ML en Cloud Dataproc

Contenido

  1. Google Cloud Dataproc Overview
    • Creating and managing clusters
    • Leveraging custom machine types and preemptible worker nodes
    • Scaling and deleting Clusters
    • Lab: Creating Hadoop Clusters with Google Cloud Dataproc
  2. Running Dataproc Jobs
    • Running Pig and Hive jobs
    • Separation of storage and compute
    • Lab: Running Hadoop and Spark Jobs with Dataproc
    • Lab: Submit and monitor jobs
  3. Integrating Dataproc with Google Cloud
    • Customize cluster with initialization actions
    • BigQuery Support
    • Lab: Leveraging Google Cloud Services
  4. Making Sense of Unstructured Data with Google’s Machine Learning APIs
    • Google’s Machine Learning APIs
    • Common ML Use Cases
    • Invoking ML APIs
    • Lab: Adding Machine Learning Capabilities to Big Data Analysis
  5. Serverless data analysis with BigQuery
    • What is BigQuery
    • Queries and Functions
    • Lab: Writing queries in BigQuery
    • Loading data into BigQuery
    • Exporting data from BigQuery
    • Lab: Loading and exporting data
    • Nested and repeated fields
    • Querying multiple tables
    • Lab: Complex queries
    • Performance and pricing
  6. Serverless, autoscaling data pipelines with Dataflow
    • The Beam programming model
    • Data pipelines in Beam Python
    • Data pipelines in Beam Java
    • Lab: Writing a Dataflow pipeline
    • Scalable Big Data processing using Beam
    • Lab: MapReduce in Dataflow
    • Incorporating additional data
    • Lab: Side inputs
    • Handling stream data
    • GCP Reference architecture
  7. Getting started with Machine Learning
    • What is machine learning (ML)
    • Effective ML: concepts, types
    • ML datasets: generalization
    • Lab: Explore and create ML datasets
  8. Building ML models with Tensorflow
    • Getting started with TensorFlow
    • Lab: Using tf.learn
    • TensorFlow graphs and loops
    • Lab: Using low-level TensorFlow + early stopping
    • Monitoring ML training
    • Lab: Charts and graphs of TensorFlow training
  9. Scaling ML models with CloudML
    • Why Cloud ML?
    • Packaging up a TensorFlow model
    • End-to-end training
    • Lab: Run a ML model locally and on cloud
  10. Feature Engineering
    • Creating good features
    • Transforming inputs
    • Synthetic features
    • Preprocessing with Cloud ML
    • Lab: Feature engineering
  11. Architecture of streaming analytics pipelines
    • Stream data processing: Challenges
    • Handling variable data volumes
    • Dealing with unordered/late data
    • Lab: Designing streaming pipeline
  12. Ingesting Variable Volumes
    • What is Cloud Pub/Sub?
    • How it works: Topics and Subscriptions
    • Lab: Simulator
  13. Implementing streaming pipelines
    • Challenges in stream processing
    • Handle late data: watermarks, triggers, accumulation
    • Lab: Stream data processing pipeline for live traffic data
  14. Streaming analytics and dashboards
    • Streaming analytics: from data to decisions
    • Querying streaming data with BigQuery
    • What is Google Data Studio?
    • Lab: build a real-time dashboard to visualize processed data
  15. High throughput and low-latency with Bigtable
    • What is Cloud Spanner?
    • Designing Bigtable schema
    • Ingesting into Bigtable
    • Lab: streaming into Bigtable

Architecting with Google Cloud: Infrastructure

Registro para Architecting with Google Cloud: Infrastructure

Objetivos

Este curso enseña a los asistentes a:

  • Elegir entre toda la gama de tecnologías que proporciona Google Cloud.
  • Métodos para desarrollo, implementación y despliegue.
  • Distinguir entre funcionalidades parecidas o relacionadas de los distintos productos y tecnologías.
  • Reconocer una gran variedad de soluciones, casos de uso y aplicaciones.
  • Desarrollar habilidades esenciales para gestionar y administrar soluciones.
  • Mejorar el conocimiento de patrones de desarrollo en cloud para asegurar la seguridad, escalabilidad, alta disponibilidad y otros elementos asociados a las aplicaciones desplegadas.

Contenido

  1. Introduction to Google Cloud
    • Google Cloud Platform (GCP) Infrastructure
    • Using GCP
    • Lab: Console and Cloud Shell
    • Demo: Projects
    • Lab: Infrastructure Preview
  2. Virtual Networks
    • Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, Firewall rules
    • Subnetworks for resource management instead of physical network topology
    • Lab: Virtual Networking
    • Lab: Bastion Host
  3. Virtual Machines
    • Compute Engine
    • Lab: Creating Virtual Machines
    • Compute options (vCPU and Memory)
    • Images
    • Common Compute Engine actions
    • Lab: Working with Virtual Machines
  4. Cloud IAM
    • Organizations, Roles, Members, Service accounts, Cloud IAM best practices
    • Lab: Cloud IAM
  5. Data Storage Services
    • Cloud Storage
    • Lab: Cloud Storage
    • Cloud SQL
    • Lab: Cloud SQL
    • Cloud Spanner, Cloud Datastore
    • Lab: Cloud Datastore
    • Cloud Bigtable
  6. Resource Management
    • Cloud Resource Manager, Quotas, Labels, Names, Billing
    • Demo: Billing Administration
    • Lab: Examining Billing Data with BigQuery
  7. Resource Monitoring
    • Stackdriver, Monitoring
    • Lab: Resource Monitoring (Stackdriver)
    • Logging, Error Reporting, Tracing, Debugging
    • Lab: Error Reporting and Debugging (Stackdriver)
  8. Interconnecting Networks
    • Cloud Virtual Private Network (VPN)
    • Lab: Virtual Private Networks (VPN)
    • Cloud Router, Cloud Interconnect, External Peering, Cloud DNS
  9. Load Balancing
    • Managed Instance Groups, HTTPS load balancing, Cross-region and content-based load balancing, SSL proxy/TCP proxy load balancing, Network load balancing
    • Lab: VM Automation and Load Balancing
  10. Autoscaling
    • Autoscaling, Policies, Configuration
    • Lab: Autoscaling
  11. Infrastructure Automation with Google Cloud Platform APIs
    • Infrastructure automation, Images, Metadata, Scripts, Google Cloud API
    • Lab: Google Cloud Platform API Infrastructure Automation
  12. Infrastructure Automation with Deployment Manager
    • Deployment Manager, Configuration, Cloud Launcher
    • Lab: Deployment Manager
  13. Managed Services
    • Cloud Dataproc, Cloud Dataflow, BigQuery, Cloud Datalab
  14. Application Infrastructure Services
    • Cloud Pub/Sub, API Management, Cloud Functions, Cloud Source Repositories, Specialty APIs
  15. Application Development Services
    • App Engine
  16. Containers
    • Containers, Kubernetes Engine, Container Registry
    • Lab: Kubernetes Load Balancing
    • Kubernetes Engine, App Engine, or Containers on Compute Engine?

Architecting with Google Cloud: Design and Process

Registro para Architecting with Google Cloud: Design and Process

Objetivos

Este curso enseña a los asistentes a:

  • Diseñar sistemas con alta disponibilidad, escalabilidad y mantenibles.
  • Integrar arquitecturas on-premise y recursos en la nube.
  • Identificar formas de optimizar recursos y minimizar costes.
  • Implementar procesos que minimicen los tiempos de parada, así como monitorizarlos y lanzar alertas, así como tests unitarios y de integración, production resilience testing y a realizar análisis post-mortem.
  • Implementar políticas para reducir los riesgos de seguridad, como auditing, separación de responsabilidades y el principio de least privilege.
  • Implementar tecnologías y procesos que aseguren la continuidad del negocio en caso de desastre.

Contenido

  1. Defining the Service
    • Design in this class.
    • State and solution.
    • Measurement.
    • Gathering requirements, SLOs, SLAs, and SLIs (key performance indicators).
  2. Business-logic layer design
    • Microservices architecture.
    • GCP 12-factor support.
    • Mapping compute needs to Google Cloud processing services.
    • Compute system provisioning.
  3. Data layer design
    • Classifying and characterizing data.
    • Data ingest and data migration.
    • Identification of storage needs and mapping to Google Cloud storage systems.
  4. Presentation layer design
    • Network edge configuration.
    • Network configuration for data transfer within the service, including load balancing and network location.
    • Network integration with other environments, including on premise and multi-cloud.
  5. Design for resiliency, scalability, and disaster recovery
    • Failure due to loss of resources.
    • Failure due to overload.
    • Strategies for coping with failure.
    • Business continuity and disaster recovery, including restore strategy and data lifecycle management.
    • Scalable and resilient design.
  6. Design for security
    • Google Cloud security.
    • Network access control and firewalls.
    • Protections against denial of service.
    • Resource sharing and isolation.
    • Data encryption and key management.
    • Identity access and auditing.
  7. Capacity planning and cost optimization
    • Capacity planning.
    • Pricing.
  8. Deployment, monitoring and alerting, and incident response
    • Deployment.
    • Monitoring and alerting.
    • Incident response.

Developing Applications with Google Cloud

Registro para Developing Applications with Google Cloud

Objetivos

Este curso enseña a los asistentes a:

  • Usar buenas prácticas para el desarrollo de aplicaciones.
  • Elegir la opción de almacenamiento apropiada para cada aplicación.
  • Implementar un control de usuarios federativo.
  • Desarrollar aplicaciones con componentes poco acoplados o microservicios.
  • Integrar componentes de una aplicación y fuentes de datos.
  • Depurar, tracear y monitorizar aplicaciones.
  • Realizar despliegues con contenedores y servicios de despliegue.
  • Elegir el entorno apropiado para cada aplicación; usar Google Kubernetes Engine como entorno y luego cambiar a una solución no-ops con Google App Engine Flex.

Contenido

  1. Best Practices for Application Development
    • Code and environment management
    • Design and development of secure, scalable, reliable, loosely coupled application components and microservices
    • Continuous integration and delivery
    • Re-architecting applications for the cloud
  2. Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
    • How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
    • Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials
  3. Overview of Data Storage Options
    • Overview of options to store application data
    • Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner
  4. Best Practices for Using Google Cloud Datastore
    • Queries
    • Built-in and composite indexes
    • Inserting and deleting data (batch operations)
    • Transactions
    • Error handling
    • Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
    • Lab: Store application data in Cloud Datastore
  5. Performing Operations on Buckets and Objects
    • Operations that can be performed on buckets and objects
    • Consistency model
    • Error handling
  6. Best Practices for Using Google Cloud Storage
    • Naming buckets for static websites and other uses
    • Naming objects (from an access distribution perspective)
    • Performance considerations
    • Setting up and debugging a CORS configuration on a bucket
    • Lab: Store files in Cloud Storage
  7. Handling Authentication and Authorization
    • Cloud Identity and Access Management (IAM) roles and service accounts
    • User authentication by using Firebase Authentication
    • User authentication and authorization by using Cloud Identity-Aware Proxy
    • Lab: Authenticate users by using Firebase Authentication
  8. Using Google Cloud Pub/Sub to Integrate Components of Your Application
    • Topics, publishers, and subscribers
    • Pull and push subscriptions
    • Use cases for Cloud Pub/Sub
    • Lab: Develop a backend service to process messages in a message queue
  9. Adding Intelligence to Your Application
    • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API
  10. Using Google Cloud Functions for Event-Driven Processing
    • Key concepts such as triggers, background functions, HTTP functions
    • Use cases
    • Developing and deploying functions
    • Logging, error reporting, and monitoring
  11. Managing APIs with Google Cloud Endpoints
    • Open API deployment configuration
    • Lab: Deploy an API for your application
  12. Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager
    • Creating and storing container images
    • Repeatable deployments with deployment configuration and templates
    • Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments
  13. Execution Environments for Your Application
    • Google Compute Engine
    • Kubernetes Engine
    • App Engine flexible environment
    • Cloud Functions
    • Cloud Dataflow
    • Lab: Deploying your application on App Engine flexible environment
  14. Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver
    • Stackdriver Debugger
    • Stackdriver Error Reporting
    • Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting
    • Stackdriver Logging
    • Key concepts related to Stackdriver Trace and Stackdriver Monitoring.
    • Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

Google Cloud Fundamentals for AWS Professionals

Registro para Google Cloud Fundamentals for AWS Professionals

Objetivos

Este curso enseña a los asistentes a:

  • Identificar los productos de GCP similares a Amazon VPC, subnets, routes, NACLs, IGW, Amazon EC2, Amazon EBS, auto-scaling, Elastic Load Balancing, Amazon S3, Amazon Glacier, Amazon RDS, Amazon Redshift, AWS IAM y más.
  • Configurar cuentas, facturación, proyectos, redes, subredes, firewalls, máquinas virtuales, discos, auto scaling, balanceadores de carga, almacenamiento, bases de datos, IAM y más.
  • Administrar y monitorizar aplicaciones.
  • Explicar las diferentes funcionalidades y modelos de precios.
  • Localizar documentación y formación.

Contenido

  1. Introducing Google Cloud
    • Google Cloud infrastructure.
    • AWS regions, availability zones, and CloudFront.
    • GCP regions, zones, edge caching, and Cloud CDN.
    • GCP services.
  2. Setting up Accounts and Billing
    • AWS accounts, billing, and IAM roles.
    • GCP accounts, billing accounts, projects, and admin setup.
    • Account, billing, project, and admin setup.
    • Lab: Set up projects and billing accounts with a free-trial GCP account.
  3. Networking
    • Amazon VPC, subnets, routes, NACLs, and security groups.
    • GCP networks, subnets, routes, and firewall rules.
    • VMs in networks.
    • Lab: Add VMs, explore the default network, and test connectivity.
  4. Working with VM Instances
    • Amazon EC2 instance types, AMIs, Amazon EBS, ephemeral drives, spot instances.
    • Google Compute Engine machine types, instances, persistent disks, local SSDs, preemptible VMs.
    • VM and web app deployment.
    • Lab: Deploy VMs with an app by console and command line.
  5. Scaling and Load Balancing Apps
    • Amazon EC2 launch configurations, auto-scaling groups, load balancing.
    • Google Compute Engine instance templates, managed instance groups, load balancing.
    • Autoscaling and load balancing setup.
    • Lab: Scale and load balance instances, and test under load.
  6. Isolating Instances and Apps
    • A 3-tier web app in GCP.
    • A custom network with custom subnets and firewall rules.
    • Lab: Build a 3-tier web app with public front-end and private backend.
  7. Using Storage as a Service and Database as a Service
    • Amazon S3, Amazon Glacier, Amazon RDS, Amazon DynamoDB, Amazon Redshift, Amazon Athena.
    • Google Cloud Storage, Google Cloud SQL, Cloud Spanner, Google Cloud Datastore, Google Cloud Bigtable, Google BigQuery.
    • Lab: Use gsutil command-line tool to perform operations on buckets and objects in Cloud Storage.
    • Lab: Load and analyze data in BigQuery.
  8. Deployment and Monitoring
    • AWS CloudFormation, Amazon CloudWatch.
    • Google Cloud Deployment Manager, Google StackDriver.
    • Lab: Deploy your infrastructure using Deployment Manager.

Getting Started with Google Kubernetes Engine

Registro para Getting Started with Google Kubernetes Engine

Objetivos

Este curso enseña a los asistentes a:

  • Entender los conceptos básicos sobre contenedores.
  • Usar contenedores en una aplicación ya existente.
  • Entender los conceptos y principios de Kubernetes.
  • Desplegar aplicaciones en Kubernetes usando línea de comandos.
  • Configurar un pipeline de despliegue contínuo usando Jenkins.

Contenido

  1. Introduction to Containers and Docker
    • Create a container.
    • Package a container using Docker.
    • Store a container image in Google Container Registry.
    • Launch a Docker container.
  2. Kubernetes Basics
    • Provision a complete Kubernetes cluster using Kubernetes Engine.
    • Deploy and manage Docker containers using kubectl.
    • Break an application into microservices using Kubernetes’ Deployments and Services.
  3. Deploying to Kubernetes
    • Create a Kubernetes deployment.
    • Trigger, pause, resume, and rollback updates.
    • Understand and build canary deployments.
  4. Continuous Deployment with Jenkins
    • Provision Jenkins in your Kubernetes cluster.
    • Create a Jenkins pipeline.
    • Implement a canary deployment using Jenkins.

Curso Oficial Spring Core

Registro para Curso Oficial Spring Core

Objetivos

Tras este curso el asistente tendrá conocimiento sobre Spring y sus tecnologías asociadas y será capaz de:

  • Usar Spring Framework para desarrollar aplicaciones Java
  • Usar la injección de dependencias para montar y configurar aplicaciones
  • Testear aplicaciones basadas en Spring
  • Configurar Spring usando XML, anotaciones y configuración Java
  • Usar Spring Data JPA y JDBC para implementar rápidamente el acceso a bases de datos relacionales
  • Usar el soporte de Spring para transacciones
  • Usar programación orientada a aspectos (AOP) para añadir funcionalidades a los objectos
  • Desarrollar aplicaciones web básicas usando Spring MVC
  • Usar Spring Security para asegurar aplicaciones web
  • Usar Spring para desarrollar fácilmente servicios REST
  • Usar Spring Boot para desarrollar aplicaciones más rápido
  • Comenzar el viaje hacia los Microservicios y Cloud Native Applications

Contenido

  1. Introduction to Spring
    • Java configuration and the Spring application context
    • @Configuration and @Bean annotations
    • @Import: working with multiple configuration files
    • Launching a Spring Application and obtaining Beans
  2. Spring Java configuration: A deeper look
    • External properties & Property sources
    • Environment abstraction
    • Bean scope, bean profiles
    • Spring Expression Language (SpEL)
    • How it Works: Inheritance based proxies
  3. Annotation-based dependency injection
    • Autowiring and component scanning
    • Java configuration versus annotations, mixing.
    • Lifecycle annotations: @PostConstruct and @PreDestroy
    • Stereotypes and meta-annotations
  4. XML dependency injection
    • XML syntax, constructor & setter injection
    • Resource prefixes
    • Namespaces and best practices when using XML
    • XML profile selection
  5. The bean lifecycle: How does Spring work internally?
    • The init phase: available interceptors
    • The init phase: what is the difference between XML, annotations and Java configuration?
    • The use and destruction phases
  6. Testing a Spring-based application
    • Spring and Test Driven Development
    • @ContextConfiguration and @RunWith annotations
    • Application context caching and the @DirtiesContext annotation
    • Profile selection with @ActiveProfiles
    • Easy test data setup with @Sql
  7. Aspect-oriented programming
    • What problems does AOP solve?
    • Differences between Spring AOP and AspectJ
    • Defining pointcut expressions
    • Implementing an advice: @Around, @Before, @After
  8. Data access and JDBC with Spring
    • How Spring integrates with existing data access technologies
    • DataAccessException hierarchy
    • Implementing caching using @Cacheable
    • jdbc namespace and Spring‘s JdbcTemplate
  9. Database transactions with Spring
    • @Transactional annotation
    • Transactions configuration: XML versus annotations
    • Isolation levels, transaction propagation and rollback rules
    • Transactions and integration testing
    • Should you use read-only transactions?
  10. JPA with Spring and Spring Data
    • Quick introduction to ORM with JPA
    • Benefits of using Spring with JPA
    • JPA configuration in Spring
    • Spring Data JPA dynamic repositories
  11. Spring in a Web application
    • Configuring Spring in a Web application
    • Introduction to Spring MVC, required configuration
    • Controller method signatures
    • Views and ViewResolvers
    • Using @Controller and @RequestMapping annotations
  12. Spring Boot
    • Using Spring Boot to bypass most configuration
    • Simplified dependency management with starter POMs
    • Packaging options, JAR or WAR
    • Easily overriding Spring Boot defaults
  13. Spring Boot - Going further
    • Going beyond the default settings
    • Customizing Spring Boot configuration
    • Logging control
    • Configuration properties using YAML
    • Boot-driven testing
  14. Spring Security
    • What problems does Spring Security solve?
    • Configuring authentication and intercepting URLs
    • The Spring Security tag library for JSPs
    • Security at the method level
    • Customizing the Spring Security filter chain
  15. REST with Spring MVC
    • An introduction to the REST architectural style
    • Controlling HTTP response codes with @ResponseStatus
    • Implementing REST with Spring MVC, @RequestBody, @ResponseBody
    • Spring MVC’s HttpMessageConverters and automatic content negotiation
  16. Microservices with Spring Cloud
    • Microservice Architectures
    • Challenges with cloud-native applications
    • Using Spring Cloud
    • Developing a simple microservice system

Curso Oficial de Spring Web

Registro para Curso Oficial de Spring Web

Objetivos

Al completar este curso, el asistente tendrá conocimiento de Spring y sus tecnologías asociadas para el desarrollo web y será capaz de:

  • Desarrollar aplicaciones web usando Spring Framework
  • Usar Spring Tool Suite
  • Implementar servidores RESTful usando Spring MVC
  • Usar Spring Boot para construir aplicaciones rápidamente con configuración automática
  • Asegurar aplicaciones web con Spring Security
  • Testear aplicaciones web en lógica y rendimiento
  • Entender y usar Web Sockets con Spring MVC

Contenido

  1. Spring overview
    • Introduction to Spring configuration
    • Bean life cycle
    • Configuration alternatives
    • Integration testing with Spring
  2. Getting started with Spring Web MVC
    • Spring model-view-controller (MVC) overview
    • DispatcherServlet
    • Controller programming model overview
    • Spring MVC views
    • Simplifying configuration
  3. Spring MVC configuration I
    • XML configuration and the mvc namespace
    • Using Java Configuration as an alternative to XML configuration
    • Running in a Servlet 3 environment without web.xml
    • Interceptors
    • Message sources
  4. Managing layouts in Spring MVC
    • Page layout and structure
    • Creating reusable templates with Apache Tiles
    • Configuring Tiles in Spring MVC
  5. Spring MVC configuration II
    • Resource configuration and the resource pipeline
    • CORS and @CrossOrigin
    • Spring MVC infrastructure Beans
    • URL mappings
    • Handler mappers and handler adapters
  6. Using views in Spring MVC
    • Views and view resolvers
    • Setting up a View resolver chain
    • Alternating views and Content Negotiation
    • JSON and XML Views
  7. Form handling with Spring MVC
    • Form Rendering
    • Type Conversion
    • Data Binding
    • Form submission lifecycle
    • Form validation (using Spring and JSR 330 validation)
    • Form Object management
  8. Site personalization with Spring MVC
    • Working in several languages: internationalization support in Spring MVC
    • Look-and-feel changes using themes and locales
    • Handling Mobile Devices with Spring Mobile
  9. Implementing REST
    • Overview of REST and HATEOAS concepts
    • Using Spring’s RestTemplate for clients access
    • Extending Spring MVC to support RESTful interactions
    • HAL and the Spring HATEAOS project
  10. Exception handling
    • Using @RequestStatus with Exceptions
    • Adding Exception handlers to Controllers
    • Global exception handling using Controller Advices and Exception resolvers
    • Exception handling for RESTful interactions
  11. Building client applications with Ajax
    • Ajax and Spring MVC
    • Using JavaScript frameworks
    • Example: Spring MVC REST and jQuery
    • Creating custom tags to encapsulate JavaScript
  12. Web application security with Spring Security
    • Motivation for Spring Security
    • Spring Security in a Web environment
    • Using Spring Security tag libraries
    • Method security
  13. Debugging and testing Web applications
    • Debugging applications in a browser
    • Testing Web applications using Spring’s Mock MVC framework
    • Using Spring HtmlUnit
  14. Spring Boot
    • Fast development and deployment using Spring Boot
    • Simplified dependency management with starter POMs
    • Packaging options - JAR or WAR
    • Easily overriding Spring Boot defaults
  15. Spring Websockets
    • Overview of Websocket development
    • Using Websockets and Stomp with Spring MVC

Enterprise Spring

Registro para Enterprise Spring

Objetivos

Al completar este curso, el asistente tendrá conocimientos de Spring y sus tecnologías asociadas para la integración empresarial y será capaz de:

  • Usar Spring para crear aplicaciones concurrentes y planificar tareas
  • Crear y consumir servicios REST
  • Usar JMS para comunicaciones asíncronas
  • Entender y utilizar transacciones distribuidas
  • Usar Spring Batch para la integración entre sistemas basados en procesos batch
  • Utilizar Spring Integration para crear arquitecturas orientadas a eventos e integradas con sistemas externos
  • Usar configuración DSL
  • Tener un conocimiento básico de Spring XD

Contenido

  1. Styles of Enterprise Integration
    • Integration Styles Pros/Cons
    • Spring Support
  2. Tasks and Scheduling
    • Introduction to concurrency
    • Java Concurrency support
    • Spring’s task scheduling support
  3. REST Webservices
  4. Spring Integration configuration
    • Using the DSL
  5. Spring Integration advanced features
    • Splitting and aggregating
    • Dispatcher configuration
  6. Introduction to Spring Batch
    • Batch concepts
    • High-Level overview
    • Job parameters and job identity
    • Quick start using Spring Batch
    • Readers, Writers & Processors
    • JDBC Item Readers
  7. Spring Batch restart and recovery
    • ExecutionContext
    • Reading flat files
    • Sharing state between steps
    • Intro to skip, retry, repeat and restart
    • Listeners
    • Business logic delegation
    • Using Java Configuration
  8. Spring Batch admin and parallel processing
    • Spring Batch Admin
    • Scaling and parallel processing
  9. Spring XD
    • Spring XD for Batch Jobs, Integration flow and Data Ingestion
    • Spring XD Installation
    • Working with Streams (Definition, Source vs Sink, deployment, use-cases)
    • Working with Jobs (Definition, deployment, monitoring, use-cases)