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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Stream Processing with Apache Flink

In this chapter, we will look at stream processing using Apache Flink and how the framework can be used to process data as soon as it arrives to build exciting real-time applications. We will start with the DataStream API and look at various operations that can be performed.

We will be looking at the following:

  • Data processing using the DataStream API 
  • Transformations 
  • Aggregations 
  • Window 
  • Physical partitioning 
  • Rescaling 
  • Data sinks
  • Event time and watermarks 
  • Kafka connector 
  • Twitter connector 
  • Elasticsearch connector 
  • Cassandra connector 
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