Apache Flink has seen increasing adoption since our initial assessment on 2016. Flink is recognized as the leading stream-processing engine and also gradually matured in the fields of batch processing and machine learning. One of Flink's key differentiator from other stream-processing engines is its use of consistent checkpoints of an application's state. In the event of failure, the application is restarted and its state is loaded from the latest checkpoint — so that the application can continue processing as if the failure had never happened. This helps us to reduce complexity of building and operating external systems for fault tolerance. We see more and more companies using Flink to build their data-processing platform.
Interest continues to build for Apache Flink, 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.
Apache Flink 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.
