Apache Flink

Process Big Data with Apache Flink

Apache Flink is an open-source framework for distributed stream and batch data processing. It provides fast, consistent, and fault-tolerant distributed processing of large volumes of data at scale. Apache Flink is designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

Flink's core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over a variety of data sources. It enables users to create efficient and reliable applications that process unbounded streams of data in real time. In addition, it also provides APIs for programming multiple computational primitives such as MapReduce, Join operators, windowing functions etc.

Apache Flink offers an extensive library of APIs including the DataStream API for stream processing applications; the DataSet API for batch processing applications; the Table API for SQL-like queries; Gelly API for graph processing; Machine Learning library; Event Time support; Connectors to external systems like Kafka or HDFS; as well as many more features. With its wide range of features, Apache Flink can be used for a variety of use cases including ETL pipelines, machine learning jobs or analytics applications.

Apache Flink has been adopted by many organisations across different industries due to its scalability and reliability features. Its capabilities make it a great choice to handle large volumes of streamed data efficiently while providing low latency performance when compared to other technologies such as Apache Spark or Hadoop MapReduce. In addition, it supports various programming languages such as Java/Scala/Python making it easier to use with existing code bases.

Overall Apache Flink is an efficient open source framework that allows users to process big datasets in real time with low latency performance across different use cases with its wide range of features such as DataStream API, DataSet API etc.


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