En esta segunda parte de la serie de cursos sobre Dataflow, analizaremos en profundidad el desarrollo de canalizaciones con el SDK de Beam. Comenzaremos con un repaso de los conceptos de Apache Beam. A continuación, analizaremos el procesamiento de datos de transmisión con ventanas, marcas de agua y activadores. Luego, revisaremos las opciones de fuentes y receptores en sus canalizaciones, los esquemas para expresar datos estructurados y cómo realizar transformaciones con estado mediante las API de State y de Timer. Después, revisaremos las prácticas recomendadas que ayudan a maximizar el rendimiento de las canalizaciones. Al final del curso, presentaremos SQL y Dataframes para representar su lógica empresarial en Beam y cómo desarrollar canalizaciones de forma iterativa con notebooks de Beam.
This course is part of the Serverless Data Processing with Dataflow en Español Specialization
Serverless Data Processing with Dataflow: Develop Pipelines en Español
About this Course
Skills you will gain
- Data Model
- Extraction, Transformation And Loading (ETL)
- State (Computer Science)
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
En este módulo, se presenta el curso y su descripción
Revisión de conceptos de Beam
Revise los conceptos principales de Apache Beam y cómo aplicarlos para escribir sus propias canalizaciones de procesamiento de datos.
Ventanas, marcas de agua y activadores
En este módulo, aprenderá a procesar datos en transmisiones con Dataflow. Para ello, debe conocer tres conceptos principales: cómo agrupar datos en ventanas, la importancia de las marcas de agua para saber cuándo la ventana está lista para producir resultados, y cómo puede controlar cuándo y cuántas veces los emitirá la ventana.
Fuentes y receptores
En este módulo, aprenderá acerca de las características de las fuentes y los receptores en Google Cloud Dataflow. En el módulo hay algunos ejemplos de E/S de Text, E/S de File, E/S de BigQuery, E/S de PubSub, E/S de KafKa, E/S de BigTable, E/S de Avro y DoFn divisible. En el módulo también se indican algunas funciones útiles asociadas a cada E/S.
En este módulo, se presentarán los esquemas, que les proporcionan a los desarrolladores una manera de expresar datos estructurados en sus canalizaciones de Beam.
Estado y Temporizadores
Este módulo abarca Estado y Temporizadores, dos funciones potentes que puede usar en su DoFn para implementar transformaciones con estado.
En este módulo, analizaremos las prácticas recomendadas y revisaremos patrones comunes que maximizan el rendimiento de sus canalizaciones de Dataflow.
Dataflow SQL y DataFrames
En este módulo, se mencionan dos API nuevas para representar su lógica empresarial en Beam: SQL y Dataframes.
Notebooks de Beam
Este módulo abarcará notebooks de Beam, una interfaz para desarrolladores de Python a fin de realizar incorporaciones en el SDK de Beam y desarrollar sus canalizaciones iterativamente en un entorno de notebooks Jupyter.
En este módulo, se ofrece un resumen del curso.
About the Serverless Data Processing with Dataflow en Español Specialization
Cada vez es más difícil mantener una pila de tecnología que pueda mantenerse al día con las crecientes demandas de un negocio basado en datos. Todos los profesionales de Big Data están familiarizados con las tres V de Big Data: volumen, velocidad y variedad. ¿Qué pasaría si existiera una tecnología a prueba de incrustaciones diseñada para satisfacer estas demandas? Ingrese al flujo de datos de la nube de Google. Google Cloud Dataflow simplifica el procesamiento de datos al unificar el procesamiento por lotes y de transmisión y brindar una experiencia sin servidor que permite a los usuarios concentrarse en el análisis, no en la infraestructura. Esta especialización está destinada a clientes y socios que buscan mejorar su comprensión de Dataflow para avanzar en sus aplicaciones de procesamiento de datos. Esta especialización contiene tres cursos:
Foundations, que explica cómo Apache Beam y Dataflow trabajan juntos para satisfacer sus necesidades de procesamiento de datos sin el riesgo de depender de un proveedor. Developing Pipelines, que cubre cómo convertir nuestra lógica comercial en aplicaciones de procesamiento de datos que pueden ejecutarse en Dataflow Operations, que repasa las lecciones más importantes para operar una aplicación de datos en Dataflow, incluido el monitoreo, la resolución de problemas, las pruebas y la confiabilidad.
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Last Updated: September 1, 2015
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