Serverless Data Processing with Dataflow: Develop Pipelines en Español

Introducción al curso

• Gain insights into the course's overview and access essential resources and feedback mechanisms.

Conceptos básicos de Beam

• Explore the foundational concepts of Apache Beam and the lifecycle of DoFn, and delve into the development of ETL pipelines with Java and Python through practical labs.

Ventanas, marcas de agua y activadores

• Delve into the fundamentals of stream processing, including windows, watermarks, and triggers, and apply your knowledge through hands-on labs for batch and stream analysis.

Fuentes y receptores

• Understand the significance of data sources and receivers, exploring various input and output options such as BigQuery, PubSub, Kafka, and more.

Esquemas

• Learn to express structured data using Beam schemas, and engage in labs to implement branching pipelines and custom flexible templates.

API de State y API de Timer

• Master stateful transformations using State and Timer APIs, enabling efficient management of state and time-based operations within your pipelines.

Prácticas Recomendadas

• Discover best practices for optimizing pipeline performance, handling unprocessable data, and leveraging advanced techniques for data processing.

Dataflow y SQL de Beam y Dataframes

• Unlock the potential of Dataflow SQL and Dataframes for representing business logic within Beam, and develop iterative pipelines for batch and stream analysis.

Notebooks de Beam

• Explore the use of Beam notebooks, empowering you to develop and iterate data pipelines effectively through an interactive environment.

Resumen del curso

• Recap the key learnings and takeaways from the entire course, solidifying your understanding of serverless data processing with Dataflow.