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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

Batch analytics


Batch Analytics in Apache Flink are quite similar to the streaming analytics in the way Flink handles both types of analytics using same APIs. This gives a lot of flexibility and allows code reuse across both the different types of analytics.

In this section, we will look at some analytical jobs on the sample data we are using OnlineRetail.csv. We will also be loading cities.csv and temperature.csv to do some more join operations.

Reading file

Flink comes with several built-in formats to create data sets from common file formats. Many of them have shortcut methods on the execution environment.

File-based

File based sources can be read using APIs which are listed as follows:

  • readTextFile(path)/TextInputFormat: Reads files line wise and returns them as strings.
  • readTextFileWithValue(path)/TextValueInputFormat: Reads files line wise and returns them as StringValues. StringValues are mutable strings.
  • readCsvFile(path)/CsvInputFormat: Parses files of comma (or another char) delimited...
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