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Big Data Analytics with Hadoop 3

You're reading from  Big Data Analytics with Hadoop 3

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Pages 482 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla

Table of Contents (18) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Introduction to Hadoop Overview of Big Data Analytics Big Data Processing with MapReduce Scientific Computing and Big Data Analysis with Python and Hadoop Statistical Big Data Computing with R and Hadoop Batch Analytics with Apache Spark Real-Time Analytics with Apache Spark Batch Analytics with Apache Flink Stream Processing with Apache Flink Visualizing Big Data Introduction to Cloud Computing Using Amazon Web Services Index

Introduction to Apache Flink


Flink is an open source framework for distributed stream processing, and has the following features:

  • It provides results that are accurate, even in the case of out-of-order or late-arriving data
  • It is stateful and fault tolerant, and can seamlessly recover from failures while maintaining an exactly-once application state
  • It performs at a large scale, running on thousands of nodes with very good throughput and latency characteristics

The following is a screenshot from the official documentation that shows how Apache Flink can be used:

Another way of viewing the Apache Flink framework is shown in the following screenshot:

All Flink programs are executed lazily, when the program’s main method is executed, the data loading and transformations do not happen directly. Rather, each operation is created and added to the program’s plan. The operations are actually executed when the execution is explicitly triggered by an execute() call on the execution environment. Whether...

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