Flink: Difference between revisions

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=Overview=
=Overview=
Apache Flink is a fourth generation data processing framework. It enables distributed data processing at scale. It supports both [[System_Design#Batch_Processing|batch processing]] and [[System_Design#Stream_Processing|stream processing]] and it is designed for stream processing natively. It promotes continuous streaming where event computations are triggered as soon as the event is received. That means Flink processes each event in real-time and provides very low latency.
Apache Flink is a fourth generation data processing framework. It enables distributed data processing at scale. It supports both [[System_Design#Batch_Processing|batch processing]] and [[System_Design#Stream_Processing|stream processing]] and it is designed for stream processing natively. Flink streaming seems to be superior to Spark streaming. It promotes continuous streaming where event computations are triggered as soon as the event is received. That means Flink processes each event in real-time and provides very low latency.
 
=Organizatorium=
=Organizatorium=
* The concept of a Flink application. Needs to be packaged and deployed.
* The concept of a Flink application. Needs to be packaged and deployed.
* Flink operator.
* Flink operator.
* A Flink operator state machine: https://github.com/lyft/flinkk8soperator/blob/master/docs/state_machine.md

Latest revision as of 01:38, 10 December 2021

External

Internal

Overview

Apache Flink is a fourth generation data processing framework. It enables distributed data processing at scale. It supports both batch processing and stream processing and it is designed for stream processing natively. Flink streaming seems to be superior to Spark streaming. It promotes continuous streaming where event computations are triggered as soon as the event is received. That means Flink processes each event in real-time and provides very low latency.

Organizatorium