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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Introducing web-based notebooks

We have worked with the Spark shell and applications in previous chapters. The shell provides great features, such as trying out code quickly and checking results interactively. However, when code becomes larger, it is difficult to edit some lines and re-execute the code. This is where applications are useful in which the entire script is saved in a file and submitted. However, in this way, you lose powerful Read, Evaluate, Print, and Loop (REPL) features of the shell. Notebooks solve this problem by providing features of both the shell and application in a web browser.

Web-based notebooks are files that contain the input code and output such as results and graphs from an interactive session. They also contain additional information, such as documentation, mathematical expressions, and media related to an interactive session. They are stored in the JSON format and can be shared with anybody across the organization or externally. It is easy to view the existing...

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