ha visto una creciente adopci¨®n desde nuestra evaluaci¨®n inicial en 2016. Flink es reconocido como el motor l¨ªder de procesamiento de flujos y tambi¨¦n ha madurado gradualmente en los campos del procesamiento por lotes (batch) y del machine learning. Uno de los diferenciadores clave de Flink con respecto a otros motores de procesamiento de flujos es el uso de puntos de control consistentes del estado de una aplicaci¨®n. En caso de fallo, la aplicaci¨®n se reinicia y su estado se carga desde el ¨²ltimo punto de control, de modo que la aplicaci¨®n puede seguir procesando como si el fallo nunca hubiera ocurrido. Esto nos ayuda a reducir la complejidad de la construcci¨®n y operaci¨®n de sistemas externos para la tolerancia a fallos. Vemos cada vez m¨¢s empresas que utilizan Flink para construir su plataforma de procesamiento de datos.
Interest continues to build for , a new-generation platform for scalable distributed batch and stream processing. At the core of Apache Flink is a streaming data-flow engine, with support for tabular (SQL-like), graph-processing and machine learning operations. Apache Flink stands out with feature rich capabilities for stream processing: event time, rich streaming window operations, fault tolerance and exactly-once semantics. The project shows significant ongoing activity, with the latest release (1.1) introducing new datasource/sink integrations as well as improved streaming features.
is a new-generation platform for scalable distributed batch and stream processing. At its core is a streaming data-flow engine. It also supports tabular (SQL-like), graph-processing and machine-learning operations. Apache Flink stands out with feature-rich capabilities for stream processing: event time, rich streaming window operations, fault tolerance and exactly-once semantics. While it hasn't reached version 1.0, it has raised significant community interest due to innovations in stream processing, memory handling, state management and simplicity of configuration.
