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Google Cloud Training

El procesamiento de datos de transmisión es cada vez más popular, puesto que permite a las empresas obtener métricas en tiempo real sobre las operaciones comerciales. Este curso aborda cómo crear canalizaciones de datos de transmisión en Google Cloud. Pub/Sub se describe para manejar los datos de transmisión entrantes. El curso también aborda cómo aplicar agregaciones y transformaciones a los datos de transmisión con Dataflow y cómo almacenar los registros procesados en BigQuery o Cloud Bigtable para analizarlos. Los estudiantes obtendrán experiencia práctica en la compilación de componentes de canalizaciones de datos de transmisión en Google Cloud con Qwiklabs.

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What's inside

Syllabus

Introducción
Este módulo es una introducción al curso y el temario
Introducción al procesamiento de datos de transmisión
Este módulo trata sobre los desafíos del procesamiento de datos de transmisión
Read more
Mensajería sin servidores con Pub/Sub
Este módulo trata sobre el uso de Pub/Sub para transferir datos de transmisión entrantes
Funciones de transmisión de Dataflow
En este módulo, se volverá a hablar de Dataflow, con un enfoque en sus capacidades de procesamiento de datos de transmisión
Transmisión con alta capacidad de procesamiento con Cloud Bigtable
Este módulo trata sobre BigQuery y Bigtable para la transmisión de datos
Funcionalidad y rendimiento avanzados de BigQuery
En este módulo, se analizan funciones más avanzadas de BigQuery
Resumen del curso
Este módulo es un resumen de los temas abordados en el curso

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Presenta una estructura escalonada, ideal para principiantes y estudiantes intermedios
Profundiza en las funciones de transmisión de Dataflow, que son esenciales para el procesamiento de datos de transmisión
Explora BigQuery y Bigtable, dos herramientas de almacenamiento de datos de Google Cloud que son valiosas para el procesamiento de transmisión
Los laboratorios prácticos de Qwiklabs brindan experiencia práctica en la creación de canalizaciones de datos de transmisión
Requiere experiencia previa en el procesamiento de datos de transmisión
El uso de Pub/Sub para la mensajería sin servidor puede requerir conocimientos previos

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Reviews summary

Positive streaming analytics in gcp en español

Overall, the course is well received by students who have reviewed it. Students overwhelmingly gave this course 5-star reviews and one 4-star review. One student noted that the course would be improved with the addition of information about optimizing GCP usage in the course title.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Resilient Streaming Analytics Systems on GCP en Español with these activities:
Repaso de fundamentos de mensajería sin servidor
Refresca tus conocimientos sobre mensajería sin servidor y la función de Pub/Sub en el procesamiento de datos de transmisión.
Browse courses on Pub/Sub
Show steps
  • Revisa la documentación de Pub/Sub
  • Realiza ejercicios prácticos de creación y publicación de mensajes
Refresca tus conocimientos en bases de datos
Revisa los conceptos básicos de bases de datos para sentar una base sólida para este curso.
Show steps
  • Lee los capítulos 1-3 del libro
  • Haz ejercicios de práctica para comprobar tu comprensión
Explora tutoriales sobre Pub/Sub
Familiarízate con Pub/Sub y sus funciones para procesar datos en tiempo real.
Browse courses on Pub/Sub
Show steps
  • Encuentra tutoriales en línea o en la documentación oficial
  • Sigue los tutoriales y experimenta con Pub/Sub
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practica transformaciones de datos con Dataflow
Fortalece tus habilidades para transformar y agregar datos en tiempo real utilizando Dataflow.
Browse courses on Dataflow
Show steps
  • Encuentra conjuntos de datos de muestra
  • Crea canalizaciones de transformación de datos en Dataflow
  • Experimenta con diferentes transformaciones
Ejercicios guiados para crear canalizaciones en Dataflow
Refuerza tu comprensión de cómo utilizar Dataflow para crear canalizaciones de datos de transmisión.
Browse courses on Dataflow
Show steps
  • Sigue un tutorial guiado sobre la creación de canalizaciones de Dataflow
  • Aplica transformaciones y agregaciones a los datos de transmisión
  • Implementa patrones de canalización comunes
Desarrolla una canalización de datos de muestra
Aplica los conceptos aprendidos para crear una canalización de datos funcional y procesar datos en tiempo real.
Browse courses on Data Engineering
Show steps
  • Define el alcance y los objetivos de la canalización
  • Diseña la arquitectura de la canalización
  • Implementa la canalización utilizando Google Cloud
  • Prueba y valida la canalización
Tutoriales sobre el uso de Cloud Bigtable y BigQuery para el procesamiento de la transmisión
Amplía tus conocimientos sobre el uso de Bigtable y BigQuery para almacenar y analizar datos de transmisión de forma eficiente.
Browse courses on Bigtable
Show steps
  • Mira tutoriales en vídeo sobre Bigtable y BigQuery
  • Experimenta con el almacenamiento de datos de transmisión en Bigtable y BigQuery
  • Explora las funciones avanzadas de Bigtable y BigQuery
Contribuye a proyectos de código abierto relacionados con el procesamiento de datos en tiempo real
Participa en proyectos de código abierto para ampliar tus conocimientos y contribuir a la comunidad.
Browse courses on Open Source
Show steps
  • Identifica proyectos relevantes en plataformas como GitHub
  • Revisa la documentación y contribuye con cambios
  • Interactúa con la comunidad y aprende de otros colaboradores

Career center

Learners who complete Building Resilient Streaming Analytics Systems on GCP en Español will develop knowledge and skills that may be useful to these careers:
Big Data Engineer
Big Data Engineers design and manage big data systems. They work with large datasets to extract insights and make data-driven decisions. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Big Data Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They develop and use models to predict future trends and patterns, identify risks and opportunities, and optimize business decisions. This course on Building Resilient Streaming Analytics Systems on GCP may be useful for someone who wants to become a Data Scientist because it provides an overview of how to use Google Cloud's tools for streaming data analytics.
Data Engineer
Data Engineers create and maintain the pipelines that capture, store, and process the data used in a business' operations. They develop and maintain the systems that ensure that data is available to data scientists and analysts for reporting and research purposes. This course on Building Resilient Streaming Analytics Systems on GCP may be useful for someone who wants to become a Data Engineer because it provides an overview of how to build streaming data pipelines using Google Cloud.
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with developers and other IT professionals to ensure that cloud-based applications and services are secure, reliable, and scalable. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Cloud Architect because it provides training on how to use Google Cloud's tools for streaming data analytics.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Software Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.
Systems Engineer
Systems Engineers design, build, and maintain computer systems. They work with a variety of hardware and software technologies to ensure that systems are reliable, efficient, and secure. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Systems Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.
DevOps Engineer
DevOps Engineers work with developers and operations teams to ensure that software is delivered quickly and reliably. They automate and streamline the software development and delivery process. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a DevOps Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns, and make recommendations for improvement. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Data Analyst because it provides training on how to use Google Cloud's tools for streaming data analytics.
Project Manager
Project Managers plan, execute, and close projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the required quality standards. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Project Manager because it provides an overview of how to use Google Cloud's tools for streaming data analytics.
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. They work with a variety of stakeholders to gather requirements, develop solutions, and implement change. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Business Analyst because it provides an overview of how to use Google Cloud's tools for streaming data analytics.
IT Manager
IT Managers plan, implement, and manage IT systems. They work with a variety of IT technologies to ensure that systems are aligned with the business's needs. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become an IT Manager because it provides an overview of how to use Google Cloud's tools for streaming data analytics.
Security Engineer
Security Engineers design, implement, and maintain security systems. They work with a variety of security technologies to ensure that systems are protected from unauthorized access and cyberattacks. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Security Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.
Database Administrator
Database Administrators design, implement, and maintain database systems. They work with a variety of database technologies to ensure that data is stored, managed, and retrieved efficiently. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Database Administrator because it provides training on how to use Google Cloud's tools for streaming data analytics.
Product Manager
Product Managers define, develop, and launch products. They work with a variety of stakeholders to ensure that products meet the needs of users and the business. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Product Manager because it provides an overview of how to use Google Cloud's tools for streaming data analytics.
Network Engineer
Network Engineers design, build, and maintain computer networks. They work with a variety of networking technologies to ensure that networks are reliable, secure, and efficient. This Building Resilient Streaming Analytics Systems on GCP course may be useful for someone who wants to become a Network Engineer because it provides training on how to use Google Cloud's tools for streaming data analytics.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Building Resilient Streaming Analytics Systems on GCP en Español.
Provides a comprehensive introduction to Kubernetes, a popular open-source container orchestration platform. It covers the basics of Kubernetes, as well as more advanced topics such as cluster management, service discovery, and security.
Provides a comprehensive introduction to cloud native patterns, covering topics such as microservices, containers, and serverless computing. It also discusses best practices for developing and deploying cloud native applications.
Provides an introduction to MapReduce, a programming model for processing large datasets. It covers the basics of MapReduce, as well as more advanced topics such as fault tolerance, data compression, and optimization.
Provides a comprehensive introduction to modern Java EE applications, covering topics such as microservices, cloud computing, and DevOps. It also discusses performance optimization techniques, such as caching, compression, and load balancing.
Provides a comprehensive introduction to browser networking, covering topics such as HTTP, TCP, and TLS. It also discusses performance optimization techniques, such as caching, compression, and load balancing.
Provides a comprehensive introduction to TensorFlow, a popular open-source machine learning library. It covers the basics of TensorFlow, as well as more advanced topics such as deep learning, neural networks, and natural language processing.
Provides a practical introduction to machine learning, using Python. It covers the basics of machine learning, as well as more advanced topics such as supervised and unsupervised learning, regression, and classification.
Provides a practical introduction to deep learning, using Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and natural language processing.

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