Fundación Tripartita
Subvencionable en España por la Fundación Tripartita Estos cursos son subvencionables en España a través de la Fundación Tripartita. Otros países podrían tener otras condiciones, por favor contáctanos para más detalles.
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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 Platform
  • Interactuar con los servicios de Google Cloud Platform
  • Describir formas en que los clientes han utilizado Google Cloud Platform
  • Elegir un entorno de despliegue adecuando en Google Cloud Platform: Google App Engine, Google Container Engine o Google Compute Engine
  • Desplegar una aplicación en Google App Engine, Google Container Engine y Google Compute Engine
  • Usar y elegir entre las diferentes opciones de almacenamiento de Google Cloud Platform: 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.

Contenido

  1. Introducing Google Cloud Platform
    • Explain the advantages of Google Cloud Platform
    • 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)
    • Lab: Sign Up for the Free Trial and Create a Project
  2. Getting Started with Google Cloud Platform
    • Identify the purpose of projects on Google Cloud Platform
    • Understand the purpose of and use cases for Identity and Access Management
    • List the methods of interacting with Google Cloud Platform
    • Lab: Getting Started with Google Cloud Platform
  3. Google App Engine and Google Cloud Datastore
    • Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore
    • Contrast the App Engine Standard environment with the App Engine Flexible environment
    • Understand the purpose of and use cases for Google Cloud Endpoints
    • Lab: Deploying Applications Using App Engine and Cloud Datastore
  4. Google Cloud Platform Storage Options
    • Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable
    • Learn how to choose between the various storage options on Google Cloud Platform
    • Lab: Integrating Applications with Google Cloud Storage
  5. Google Container Engine
    • Define the concept of a container and identify uses for containers
    • Identify the purpose of and use cases for Google Container Engine and Kubernetes
  6. Google Compute Engine and Networking
    • Identify the purpose of and use cases for Google Compute Engine
    • Understand the various Google Cloud Platform networking and operational tools and services
    • Lab: Deploying Applications Using Google Compute Engine
  7. Big Data and Machine Learning
    • Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms
    • Lab: Getting Started with BigQuery

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 Platform
  • Usar CloudSQL y Cloud Dataproc para migrar datos de MySQL y Hadoop/Pig/Spark/Hive a Google Cloud Platform
  • 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 Platform

Contenido

  1. Introducing Google Cloud Platform
    • Google Platform Fundamentals Overview
    • Google Cloud Platform Data Products and Technology
    • Usage scenarios
    • Lab: Sign up for Google Cloud Platform
  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
    • Machine Learning with TensorFlow
    • Lab: Train and use neural network
    • Fully built models for common needs
    • Lab: Employ ML APIs
  5. Data processing architectures
    • Message-oriented architectures with Pub/Sub
    • Creating pipelines with Dataflow
    • Reference architecture for real-time and batch data processing
  6. Summary
    • Why GCP?
    • Where to go from here
    • Additional Resources

Serverless Data Analysis with BigQuery and Cloud Dataflow (CPB101)

Registro para Serverless Data Analysis with BigQuery and Cloud Dataflow (CPB101)

Objetivos

Al finalizar este curso, los participantes serán capaces de:

  • Construir consultas complejas de BigQuery usando clauses, inner selects, built-in functions y joins
  • Cargar y exportar datos hacia/desde BigQuery
  • Identificar la necesidad de campos anidados, repetidos y funciones definidas por el usuario
  • Entender el procesamiento en paralelo, términología y conceptos
  • Escribir pipelines en Java o Python y lanzarlos localmente o en la nube
  • Implementar transformaciones MapReduce en pipelines de DataFlow.
  • Unir conjuntos de datos
  • Interpolar Dataflow, BigQuery y Cloud Pub/Sub para streaming en tiempo real

Contenido

  1. Serverless data analysis with BigQuery
    • What is BigQuery?
    • Queries and functions
    • Load and export data
    • Advanced Capabilities
    • Performance and pricing
  2. Serverless, autoscaling data pipelines with Dataflow
    • What is Dataflow?
    • Data pipeline
    • MapReduce in Dataflow
    • Side inputs
    • Streaming

Machine Learning with Cloud ML (CPB102)

Registro para Machine Learning with Cloud ML (CPB102)

Objetivos

Al finalizar este curso, los participantes sabrán:

  • Entender los tipos de problemas que machine learning puede abordar
  • Construir un modelo de machine learning usando TensorFlow
  • Construir modelos de ML escalables y desplegables usando Cloud ML
  • Conocer la importancia del preprocesamiento y la combinación de funcionalidades
  • Incorporar conceptos avanzados de ML a sus modelos
  • Hacer uso de APIs de ML
  • Poner en producción un modelo de ML

Contenido

  1. Getting Started with Machine Learning
    • What is ML?
    • Effective ML
    • Evaluating ML
    • ML datasets: generalization
  2. Building ML models with TensorFlow
    • Getting started
    • TensorFlow graphs and loops
    • Monitoring
  3. Scaling ML models with Cloud ML
    • Why Cloud ML?
    • Packaging up a TensorFlow model
    • End-to-end training
  4. Feature Engineering
    • Creating good features
    • Transforming inputs
    • Synthetic features
    • Preprocessing with Cloud ML
  5. ML architectures
    • Wide and deep
    • Image analysis
    • Embeddings and sequences
    • Recommendation systems

Google Cloud Platform for Architects (CPA200)

Registro para Google Cloud Platform for Architects (CPA200)

Objetivos

Al final de este curso, los asistentes serán capaces de:

  • Entender los puntos básicos a considerar al diseñar y desplegar en la nube
  • Tener confianza suficiente para aprovechar todo lo que ofrece Google Cloud Platform
  • Comprender la forma de empezar a trabajar con Google Cloud Platform
  • Identificar los productos de Google Cloud Platform apropiados para hacer uso de patrones de aquitectura

Contenido

  1. Keeping things simple
    • Managing applications at scale
    • Describe the problems that Google addressed to allow them to deploy Google scale applications
    • Explain how using Google Cloud addresses each of the problems faced when designing for distributed scalable applications that are deployed across regions
    • Micro services
    • Security & compliance
  2. Focusing on Your Business
    • Managing applications at scale
    • Describe the problems that Google addressed to allow them to deploy Google scale applications
    • Explain how using Google Cloud addresses each of the problems faced when designing for distributed scalable applications that are deployed across regions
    • Micro services
  3. Embrace Failure
    • Decoupling
    • Self healing
    • Testing
  4. Moving to the Cloud
    • Migrating applications to Google Cloud Platform
    • Off site disaster recovery and archival with Google Cloud Platform
    • Hybrid architectures and multi cloud deployments
    • Lock in is not an issue using Google Cloud Platform
  5. Architectural patterns using Google Cloud Platform
    • Cloud Deployment manager
    • Image processing
    • Mobile applications
    • Big Data
    • Virtual network environments

Google BigQuery for Data Analysts (CPB200)

Registro para Google BigQuery for Data Analysts (CPB200)

Objetivos

Al final de este curso, los asistentes serán capaces de:

  • Entender el propósito y los casos de uso para Google BigQuery
  • Describir formas que han utilizado los usuarios para mejorar sus negocios haciendo uso de Google BigQuery
  • Entender la arquitectura de BigQuery y cómo se procesan las consultas
  • Interactuar con BigQuery usando las interfaces web y de línea de comandos
  • Identificar el propósito y la estructura de los esquemas y tipos de datos de BigQuery
  • Entender el objetivo y las ventajas de los destinations tables y el cacheo
  • Usar tareas de BigQuery
  • Transformar y cargar datos en BigQuery
  • Exportar datos de BigQuery
  • Almacenar los resultados de una consulta en un destination table.
  • Creación de consultas
  • Exportar los datos del log a BigQuery y hacer búsquedas sobre ellos.
  • Entender la estructura de precios de BigQuery y evaluar mecanismos para controlar los costes de las consultas y el almacenamiento
  • Identificar las mejores prácticas para optimizar el rendimiento de las consultas
  • Saber los problemas más comunes cuando se usa BigQuery
  • Usar varias funciones de BigQuery
  • Integrar BigQuery con herramientas externas como spreadsheets
  • Visualizar datos de BigQuery
  • Usar control de acceso para restringir el acceso a datos

Contenido

  1. Introducing Google BigQuery
    • Understand the purpose of and use cases for Google BigQuery
    • Describe ways in which customers have used Google BigQuery to improve their businesses
  2. BigQuery Functional Overview
    • Describe the components of a BigQuery project
    • Identify how BigQuery stores data and list the advantages of the storage model
    • Understand the architecture of BigQuery and how queries are processed
    • Describe the methods of interacting with BigQuery
  3. BigQuery Fundamentals
    • Describe the purpose of denormalizing data
    • Identify the purpose and structure of BigQuery schemas and data types
    • Explain the types of actions available in BigQuery jobs
    • Understand the purpose of and advantages of BigQuery destinations tables and caching
  4. Ingesting, Transforming, and Storing Data
    • Describe the methods for ingesting data, transforming data, and storing data using BigQuery
    • Explain the function of BigQuery federated queries
  5. Pricing and Quotas
    • Explain the advantages of the BigQuery pricing model
    • Use the pricing calculator to calculate storage and query costs
    • Identify the quotas that apply to BigQuery projects
  6. Clauses and Functions
    • Explain the differences between BigQuery SQL and ANSI SQL
    • Identify the purpose of and use cases for user-defined functions
    • Explain the purpose of various BigQuery functions
  7. Nested and Repeated Fields
    • Identify the purpose and structure of BigQuery nested, repeated, and nested repeated fields
    • Describe the use cases for nested, repeated, and nested repeated fields
  8. Query Performance
    • Explain the impact of the following in query performance: JOIN and GROUP BY, table wildcards, and table decorators
    • Identify various best practices for optimizing query performance
  9. Troubleshooting Errors
    • Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors
  10. Access Control
    • Describe the purpose of access control lists in BigQuery
    • List and explain the project and dataset roles available in BigQuery
    • Apply views for row-level security
  11. Exporting Data
    • List the methods of exporting data from BigQuery and the data formats available
    • Describe the process of creating a job to export data from BigQuery
    • Explain the purpose of wildcard exports to partition export data
  12. Interfacing with External Tools
    • Describe how to use external tools to interface with BigQuery, including: spreadsheets, ODBC and JDBC drivers, the BigQuery encrypted client, and R
  13. Working with Google Analytics Premium Data
    • Describe the schema of the Google Analytics Premium and AdSense data exported to BigQuery
  14. Data Visualization
    • Describe the options available for visualizing BigQuery data

Data Engineering on Google Cloud Platform

Registro para Data Engineering on Google Cloud Platform

Objetivos

Este curso enseña a los asistentes a:

  • Diseñar y construir sistemas de procesamiento de datos en Google Cloud Platform
  • 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. Serverless data analysis with BigQuery
    • What is BigQuery
    • Advanced Capabilities
    • Performance and pricing
    • Lab: Queries and Functions
    • Lab: Load and Export data
  2. Serverless, autoscaling data pipelines with Dataflow
    • Introduction to Dataflow and capabilities
    • Lab: Data pipeline
    • Lab: MapReduce in Dataflow
    • Lab: Side inputs
    • Lab: Streaming
  3. Google Cloud Dataproc Overview
    • Introducing Google Cloud Dataproc
    • Creating and managing clusters
    • Defining master and worker nodes
    • Leveraging custom machine types and preemptible worker nodes
    • Creating clusters with the Web Console
    • Scripting clusters with the CLI
    • Using the Dataproc REST API
    • Dataproc pricing
    • Scaling and deleting Clusters
    • Lab: Creating Hadoop Clusters with Google Cloud Dataproc
  4. Running Dataproc Jobs
    • Controlling application versions
    • Submitting jobs
    • Accessing HDFS and Google Cloud Storage
    • Hadoop
    • Spark and PySpark
    • Pig and Hive
    • Logging and monitoring jobs
    • Accessing onto master and worker nodes with SSH
    • Working with PySpark REPL (command-line interpreter)
    • Lab: Running Hadoop and Spark Jobs with Dataproc
  5. Integrating Dataproc with Google Cloud Platform
    • Initialization actions
    • Programming Jupyter/Datalab notebooks
    • Accessing Google Cloud Storage
    • Leveraging relational data with Google Cloud SQL
    • Reading and writing streaming Data with Google BigTable
    • Querying Data from Google BigQuery
    • Making Google API Calls from notebooks
    • Lab: Big Data Analysis with Dataproc
  6. Making Sense of Unstructured Data with Google’s Machine Learning APIs
    • Google’s Machine Learning APIs
    • Common ML Use Cases
    • Vision API
    • Natural Language API
    • Translate
    • Speech API
    • Lab: Adding Machine Learning Capabilities to Big Data Analysis
  7. Getting started with Machine Learning
    • What is machine learning (ML)
    • Effective ML: concepts, types
    • Evaluating ML
    • 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. ML architectures
    • Wide and deep
    • Image analysis
    • Lab: Custom image classification with transfer learning
    • Embeddings and sequences
    • Recommendation systems
  12. Need for real-time streaming analytics
    • What is Streaming Analytics?
    • Use-cases
    • Batch vs Streaming (Real-time)
    • Related terminologies
    • GCP products that help build for high availability, resiliency, high-throughput, real-time streaming analytics (review of Pub/Sub and Dataflow)
    • Lab: Setup project, enable APIs, setup storage
  13. Architecture of streaming pipelines
    • Streaming architectures and considerations
    • Choosing the right components
    • Lab: Explore the dataset
    • Windowing
    • Streaming aggregation
    • Events, triggers
    • Lab: Create architecture reference
  14. Stream data and events into PubSub
    • Topics and Subscriptions
    • Publishing events into Pub/Sub
    • Lab: Streaming data ingest into Pub/Sub
    • Subscribing options: Push vs Pull
    • Alerts
  15. Build a stream processing pipeline
    • Pipelines, PCollections and Transforms
    • Windows, Events, and Triggers
    • Aggregation statistics
    • Streaming analytics with BigQuery
    • Low-volume alerts
    • Lab: alerting scenario for anomalies
  16. High throughput and low-latency with Bigtable
    • Latency considerations
    • Lab: create streaming data processing pipelines with Dataflow
    • What is Bigtable?
    • Designing row keys
    • Performance considerations
    • Lab: high-volume event processing
  17. Building Dashboards
    • What is Google Data Studio?
    • From data to decisions
    • Lab: build a real-time dashboard to visualize processed data

Developing Solutions with Google Cloud Platform (CPD200)

Registro para Developing Solutions with Google Cloud Platform (CPD200)

Objetivos

Al finalizar el curso, el asistente será capaz de:

  • Construir aplicaciones fiables y escalables usando Google App Engine Standard Environment.
  • Aprovechar Google Cloud Endpoints para implementar, desplegar y administrar APIs de backend.
  • Crear aplicaciones basadas en microservicios usando los servicios de App Engine.
  • Administrar tanto la seguridad como el versionado, despliegue y monitorización de una aplicación.
  • Almacenar datos de aplicaciones en Google Cloud Datastore, así como optimizar el rendimiento de las consultas y el uso de transacciones.
  • Mejorar el rendimiento y la capacidad con Memcache y el escalado de instancias.

Contenido

  1. Developing Solutions with Google Cloud Platform
    • Benefits of Google Cloud Platform
    • Development tools and services for Google Cloud Platform
    • Google Cloud Platform solution architectures
    • Lab: Google Cloud Source Repositories
  2. Google Cloud Endpoints
    • Cloud Endpoints features
    • Developing APIs using Cloud Endpoints
    • Accessing Cloud Endpoints APIs using JavaScript clients
    • Lab: Google Cloud Endpoints
  3. App Engine Services
    • Modular application design and App Engine services
    • Deploying services
    • Accessing App Engine services
    • Lab: Google App Engine Services
  4. User Authentication and Credentials
    • Authentication and authorization concepts
    • Securing access through application configuration
    • Authentication with the Users service
    • Authorization with API keys, OAuth, and application default credentials
    • Lab: User Authentication
  5. Managing App Engine Applications
    • Deploying and managing multiple application versions
    • Traffic splitting, incremental rollouts, and canary releases
    • Budgets and quotas
    • Stackdriver logging and application tracing
    • Lab: Managing Google App Engine Applications
  6. Storage for Solution Developers
    • Functionality and benefits of Cloud Platform storage options
    • Using Google Cloud Storage for immutable BLOB storage
    • Integrating Google Cloud SQL into App Engine Apps
    • Cloud Datastore fundamentals
    • Lab: Google Cloud Datastore
  7. Queries and Indexes
    • Implementing query filters with Cloud Datastore
    • Single-property and composite indexes
    • Configuring and optimizing indexes
    • Lab: Google Cloud Datastore Queries and Indexes
  8. Entity Groups, Consistency, and Transactions
    • Strong and eventual consistency in Cloud Datastore
    • Ensuring strongly consistent queries
    • Best practices for Cloud Datastore transactions
    • Lab: Google Cloud Datastore Transactions
  9. App Engine Performance and Optimization
    • Memcache use cases and implementation patterns
    • Manual, basic, and automatic scaling behavior
    • Configuring application scaling
    • Lab: Google App Engine Performance and Optimization
  10. Task Queues
    • Push and pull queue capabilities and configuration
    • Adding and consuming tasks with push and pull queues
    • Scheduling tasks with the Cron Service
    • Lab: Task Queue API
    • Lab: Deleting Google Cloud Platform Projects and Resources

Google Cloud Platform for Systems Operations Professionals (CPO200)

Registro para Google Cloud Platform for Systems Operations Professionals (CPO200)

Objetivos

Al finalizar este curso, los asistentes serán capaces de:

  • Entender los puntos principales a considerar en un diseño y despliegue orientado a la nube
  • Usar la consola para desarrolladores para crear y administrar múltiples proyectos
  • Usar service accounts y permisos para permitir el acceso entre proyectos
  • Crear instancias de Google Compute Engine
  • Crear una red distinta a la por defecto y revisar la configuración de red
  • Comparar la red por defecto y las demás redes
  • Crear reglas de firewall con y sin etiquetas
  • Crear y usar una imagen de Compute Engine personalizada
  • Establecer scopes de autorización para una instancia de Compute Engine
  • Reservar una dirección IP externa para una instancia
  • Hacer un snapshot de una instancia de Compute Engine
  • Hacer un snapshot de un disco
  • Crear una imagen usando un disco de arranque persistente
  • Subir una imagen a Google Container Registry
  • Crear una instancia de Cloud SQL usando Cloud SDK
  • Desplegar y probar una aplicación web
  • Añadir metadatos a una instancia y un proyecto
  • Usar Cloud SDK para consultar los metadatos de una instancia y un proyecto
  • Crear una instancia usando un script de startup en los metadatos y Google Cloud Storage
  • Crear una instancia con un script de shutdown e instalar Cloud Logging agent
  • Usar API Explorer para hacer consultas a un API
  • Ejecutar código de ejemplo que use Google API Client Library
  • Construir y probar un contenedor que use Cloud SQL API
  • Crear una plantilla y administrar grupos de instancias
  • Configurar un grupo de instancias para autoescalado
  • Configurar un balanceador de carga por HTTP tolerante a fallos
  • Probar los health checks para usar en un balanceador de carga por HTTP
  • Administrar el despliegue de aplicaciones usando Jinja y plantillas de Python con Google Cloud Deployment Manager
  • Eliminar proyectos y recursos de Google Cloud Platform

Contenido

  1. Google Cloud Platform Projects
    • Identify project resources and quotas
    • Explain the purpose of Google Cloud Resource Manager and Identity and Access Management
  2. Instances
    • Explain how to create and move instances
    • Identify how to connect to and manage instances
  3. Networks
    • Explain how to create and manage networks in projects
    • Identify how to create and manage firewall rules, routes, and IP addresses
  4. Disks and Images
    • Explain how to create and manage persistent disks
    • Identify how to create and manage disk images
  5. Authorization
    • Explain the purposes of and use cases for Google Compute Engine service accounts
    • Identify the types of service account scopes
  6. Snapshots
    • Identify the purpose of and use cases for disk snapshots
    • Explain the process of creating a snapshot
  7. Google Cloud Storage
    • Explain the purpose of and use cases for Google Cloud Storage
    • Identify methods for accessing Google Cloud Storage buckets and objects
    • Explain the security options available for Google Cloud Storage buckets and objects
  8. Instance Groups
    • Identify the purpose of and use cases for instance groups
    • Explain the process of creating and using instance groups
  9. Google Cloud SQL
    • Understand how to create and administer Cloud SQL instances
    • Explain how to access Cloud SQL instances from Compute Engine instances
  10. Metadata
    • Explain the purpose of metadata and identify the use cases for project and instance metadata
    • Identify how to set and query metadata
  11. Startup and Shutdown Scripts
    • Identify the purpose of and use cases for startup and shutdown scripts
  12. Autoscaling
    • Explain the use cases for autoscaling and how autoscaling functions
    • Identify the purpose of autoscaling policies
  13. Load Balancing
    • Explain the differences between network load balancing and HTTP load balancing
    • Identify the purpose of and use cases for cross-region and content-based load balancing

Architecting with Google Cloud Platform: Infrastructure

Registro para Architecting with Google Cloud Platform: Infrastructure

Objetivos

Este curso enseña a los asistentes a::

  • Elegir entre toda la gama de tecnologías que proporciona Google Cloud Platform.
  • 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 Platform
    • Role of the Cloud Architect
    • Learn about Solution Domains as an approach to design
    • Lab: Console and Cloud Shell
  2. Virtual Networks
    • Cloud Virtual Networks (CVN), Projects, Networks, Subnetworks, IP addresses, Routes, Firewall rules
    • Subnetworks for resource management instead of physical network topology
    • Lab: Virtual Networking
  3. Virtual Machines
    • GCE, tags, VM options, vCPUs, disk options, images, and special features of persistent disks for VMs
  4. Cloud IAM
    • Members, roles, organizations, account administration, service accounts
    • Lab: Cloud IAM
  5. Resource Management
    • Billing, Quotas, Labels, Names, Cloud Resource Manager
    • Lab: Lab Billing
  6. Data Services
    • Cloud Storage, Datastore, Bigtable, Cloud SQL
    • Lab: Cloud Storage
    • Lab: Cloud SQL
  7. Interconnecting Networks
    • VPNs, Cloud Router, Cloud Interconnect, Direct Peering, Cloud DNS
    • Lab: VPN and Cloud Router
  8. Infrastructure Automation
    • Infrastructure automation, custom images, startup and shutdown scripts and metadata, Deployment Manager, Cloud Launcher
    • Lab: Hadoop Cluster Maker
    • Lab: Virtual Machine
  9. Autoscaling
    • Load Balancing, Instance Groups, Autoscaler
    • Lab: Autoscaling
  10. Resource Monitoring
    • Stackdriver, Monitoring, Logging, Error Reporting, Tracing, Debugging
    • Lab: Resource Monitoring (Stackdriver)
  11. Containers
    • Containers, Google Container Engine (GKE), and Container Registry
  12. Platform Security
    • Learn about Google's layered security strategy that uses a multi-faceted approach to provide platform security services and benefits
  13. Managed Services
    • Dataproc, Dataflow, BigQuery, Datalab
    • Lab: BigQuery and Datalab
  14. Application Development Infrastructure
    • App Engine, Cloud SDK, Dev Tools, Cloud Source Repos, Cloud Pub/Sub, Cloud Endpoints and Apigee, Cloud Functions

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)