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Serverless Data Processing with Dataflow
Operations en Español
Google Cloud Training
Google Cloud Training
En esta última parte de la serie de cursos de Dataflow, presentaremos los componentes del modelo operativo de Dataflow. Examinaremos las herramientas y técnicas que permiten solucionar problemas y optimizar el rendimiento de las canalizaciones. Luego, revisaremos las prácticas recomendadas de las pruebas, la implementación y la confiabilidad en relación con las canalizaciones de Dataflow. Concluiremos con una revisión de las plantillas, que facilitan el ajuste de escala de las canalizaciones de Dataflow para organizaciones con cientos de usuarios. Estas clases asegurarán que su plataforma de datos sea estable y resiliente ante circunstancias inesperadas.
Register for this course and see more details by visiting:
OpenCourser.com/course/cheh1p/serverless
What's inside
Syllabus
Introducción
En este módulo, se aborda la descripción del curso.
Monitoring
En este módulo, aprenderemos a usar la página Lista de trabajos para filtrar los trabajos que deseamos supervisar o investigar. Observaremos cómo las pestañas Gráfico del trabajo, Información del trabajo y Métricas del trabajo brindan en conjunto un resumen completo de su trabajo de Dataflow. Por último, aprenderemos a usar la integración de Dataflow en el Explorador de métricas a fin de crear políticas de alertas para métricas de Dataflow.
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Syllabus
Introducción
En este módulo, se aborda la descripción del curso.
Monitoring
En este módulo, aprenderemos a usar la página Lista de trabajos para filtrar los trabajos que deseamos supervisar o investigar. Observaremos cómo las pestañas Gráfico del trabajo, Información del trabajo y Métricas del trabajo brindan en conjunto un resumen completo de su trabajo de Dataflow. Por último, aprenderemos a usar la integración de Dataflow en el Explorador de métricas a fin de crear políticas de alertas para métricas de Dataflow.
Read more
Informes de errores y registros
En este módulo, aprenderemos a usar el panel Registro ubicado al final de las páginas Grafo de trabajo y Métricas del trabajo, y sobre la página Informes de errores centralizada.
Solución de problemas y depuración
En este módulo, aprenderemos a depurar canalizaciones de Dataflow y solucionar sus problemas. También revisaremos los cuatro modos habituales de fallas de Dataflow: falla en la compilación de canalizaciones, falla en el inicio de la canalización de Dataflow, falla en la ejecución de canalizaciones y problemas de rendimiento.
Rendimiento
En este módulo, analizaremos las consideraciones de rendimiento que se deben tener presentes cuando se desarrollan canalizaciones por lotes y de transmisión en Dataflow.
Testing y CI/CD
En este módulo, analizaremos cómo realizar pruebas de unidades de las canalizaciones de Dataflow. También presentaremos los frameworks y las funciones disponibles a fin de optimizar su flujo de trabajo de CI/CD para las canalizaciones de Dataflow.
Confiabilidad
En este módulo, analizaremos métodos para compilar sistemas que sean resilientes ante la corrupción de datos y las interrupciones de los centros de datos.
Plantillas Flexibles
En este módulo, se abordan las plantillas flexibles, una función que ayuda a los equipos de ingeniería a estandarizar y reutilizar el código de las canalizaciones de Dataflow. Muchos desafíos operativos se pueden solucionar con las plantillas flexibles.
Resumen
En este módulo, se revisan los temas abordados en el curso.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Esta parte de la serie de cursos de Dataflow investiga el modelo operativo de Dataflow
Examina las herramientas y técnicas que permiten solucionar problemas y optimizar el rendimiento de las canalizaciones
Estudia las prácticas recomendadas de las pruebas, la implementación y la confiabilidad en relación con las canalizaciones de Dataflow
Concluye con una revisión de las plantillas, que facilitan el ajuste de escala de las canalizaciones de Dataflow para organizaciones con cientos de usuarios
Estas clases asegurarán que su plataforma de datos sea estable y resiliente ante circunstancias inesperadas
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Activities
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We're preparing activities for
Serverless Data Processing with Dataflow: Operations en Español.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Serverless Data Processing with Dataflow: Operations en Español will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining data pipelines. They work with data analysts and data scientists to design and implement data pipelines that meet the needs of the business. This course can help you become a Data Engineer by teaching you the concepts of data processing and how to build and maintain data pipelines.
Data Analyst
Data Analyst
Data Analysts are responsible for analyzing data to find trends and patterns. They use this information to help businesses make better decisions. This course can help you become a Data Analyst by teaching you the concepts of data processing and how to analyze data to find trends and patterns.
Data Scientist
Data Scientist
Data Scientists are responsible for developing and implementing machine learning models. They use these models to help businesses solve problems and make better decisions. This course can help you become a Data Scientist by teaching you the concepts of data processing and how to develop and implement machine learning models.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work with data engineers and data scientists to develop software applications that use data to solve problems and make better decisions. This course can help you become a Software Engineer by teaching you the concepts of data processing and how to build and maintain software applications.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for applying mathematical and analytical techniques to solve problems in business and industry. They use this course to help businesses make better decisions. This course can help you become an Operations Research Analyst by teaching you the concepts of data processing and how to apply mathematical and analytical techniques to solve problems in business and industry.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They use this information to help businesses make better decisions. This course can help you become a Business Analyst by teaching you the concepts of data processing and how to analyze business processes and identify opportunities for improvement.
Market Research Analyst
Market Research Analyst
Market Research Analysts are responsible for conducting surveys and analyzing data to understand consumer behavior. They use this information to help businesses make better decisions. This course can help you become a Market Research Analyst by teaching you the concepts of data processing and how to conduct surveys and analyze data to understand consumer behavior.
Management Analyst
Management Analyst
Management Analysts are responsible for studying an organization's operations and recommending ways to improve them. They work with data engineers and data scientists to design and implement systems that use data to solve problems and make better decisions.
Systems Analyst
Systems Analyst
Systems Analysts are responsible for studying an organization's information needs and designing and implementing systems to meet those needs. They work with data engineers and data scientists to design and implement systems that use data to solve problems and make better decisions.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use this information to help businesses make better decisions. This course can help you become a Statistician by teaching you the concepts of data processing and how to collect, analyze, and interpret data.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for testing software applications to find defects. They work with software engineers and data scientists to find defects in software applications and improve their quality.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work with data engineers and data scientists to ensure that databases are running smoothly and are available to users. This course can help you become a Database Administrator by teaching you the concepts of data processing and how to manage and maintain databases.
Information Security Analyst
Information Security Analyst
Information Security Analysts are responsible for protecting an organization's data from unauthorized access. They work with data engineers and data scientists to develop and implement security measures to protect data from unauthorized access.
Computer Systems Analyst
Computer Systems Analyst
Computer Systems Analysts are responsible for studying an organization's computer systems and recommending ways to improve them. They work with data engineers and data scientists to ensure that computer systems are running smoothly and are available to users.
Network Administrator
Network Administrator
Network Administrators are responsible for managing and maintaining computer networks. They work with data engineers and data scientists to ensure that networks are running smoothly and are available to users.
For more career information including salaries, visit:
OpenCourser.com/course/cheh1p/serverless
Reading list
We've selected ten 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
Serverless Data Processing with Dataflow: Operations en Español.
Provides a comprehensive overview of machine learning, including a chapter on Dataflow. It covers topics such as data preparation, model training, and evaluation, as well as best practices for building and operating machine learning systems.
Save
Provides a comprehensive overview of deep learning, including a chapter on Dataflow. It covers topics such as data preparation, model training, and evaluation, as well as best practices for building and operating deep learning systems.
Provides a comprehensive overview of data science and machine learning, including a chapter on Dataflow. It covers topics such as data preparation, model training, and evaluation, as well as best practices for building and operating data science and machine learning systems.
Provides a comprehensive overview of machine learning with R, including a chapter on Dataflow. It covers topics such as data preparation, model training, and evaluation, as well as best practices for building and operating machine learning systems.
Provides a comprehensive overview of data visualization with Python and JavaScript. It covers topics such as data exploration, data visualization, and interactive data visualization, as well as best practices for building and operating data visualization systems.
Save
Provides a comprehensive overview of data mining with R. It covers topics such as data preparation, data mining algorithms, and data mining applications, as well as best practices for building and operating data mining systems.
Provides a comprehensive overview of statistical learning with sparsity. It covers topics such as sparse linear models, sparse regression, and sparse principal component analysis, as well as best practices for building and operating sparse learning systems.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, as well as best practices for building and operating pattern recognition and machine learning systems.
Provides a comprehensive overview of statistical learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, as well as best practices for building and operating statistical learning systems.
Save
Provides a comprehensive overview of machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, as well as best practices for building and operating machine learning systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cheh1p/serverless
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Effort
3 weeks of study, 6-8 hours/week
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Español
Transcript
English
This course belongs to a
collection
called
Serverless Data Processing with Dataflow en Español. It's comprised of three courses:
- Serverless Data Processing with Dataflow: Operations en Español (viewing)
- Serverless Data Processing with Dataflow:Foundations Español
- Serverless Data Processing with Dataflow: Develop Pipelines en Español
Good to know
Esta parte de la serie de cursos de Dataflow investiga el modelo operativo de Dataflow
Examina las herramientas y técnicas que permiten solucionar problemas y optimizar el rendimiento de las canalizaciones
Estudia las prácticas recomendadas de las pruebas, la implementación y la confiabilidad en relación con las canalizaciones de Dataflow
Concluye con una revisión de las plantillas, que facilitan el ajuste de escala de las canalizaciones de Dataflow para organizaciones con cientos de usuarios
Estas clases asegurarán que su plataforma de datos sea estable y resiliente ante circunstancias inesperadas
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