Flink Background Jobs
Apache Flink is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams.
- Since:2014
- Discord:@RFvTtNKy
- Dockerhub:flink
- Docs:ci.apache.org
- Github Topic:apache-flink
- License:www.apache.org
- Official:flink.apache.org
- Reddit:r/apacheflink
- Repository:github.com
- StackOverflow:[apache-flink]
- Twitter:@apacheflink
- Wikipedia:Apache_Flink
#What is Flink?
Apache Flink is an open-source distributed data processing system that can perform batch processing, stream processing, and iterative processing for Big Data. It was designed to provide efficient, reliable, and fast processing for massive amounts of data. It supports multiple languages including Java, Scala, and Python.
#Flink Key Features
Some of the most recognizable features of Apache Flink are:
- Fault-tolerant: Flink provides automatic fault tolerance through checkpointing and state management.
- Low-latency: Flink has a low latency processing capability of just a few milliseconds which makes it ideal for real-time data processing.
- Scalable: Flink has a distributed architecture which allows it to scale horizontally as data grows.
#Flink Use-Cases
Some of the use-cases of Apache Flink are:
- Real-time analytics: Flink can be used to perform real-time analytics on large amounts of data to provide insights into user behavior, product performance, and other key metrics.
- Fraud detection: Flink can be used to detect fraud in real-time by analyzing transactional data and identifying patterns and anomalies.
- Machine learning: Flink can be used to run machine learning algorithms on large datasets, providing insights into user behavior and identifying trends.
#Flink Summary
Apache Flink is an open-source, distributed data processing system that provides low-latency, fault-tolerant processing for large amounts of data, and can be used for real-time analytics, fraud detection, and machine learning.
