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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
Tools
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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 Jupyter


The Jupyter Notebook supports over 40 languages and integrates with Spark and Hadoop to query interactively and visualize results with ggplot2, matplotlib, and others.

The Jupyter project evolved from the IPython project. The IPython project has accumulated many languages other than Python over a period of time. As a result, the IPython name became irrelevant for the project, so the name has been changed to Jupyter with inspiration from the Julia, Python, and R languages. IPython will continue to exist as a Python kernel for Jupyter. In simple words, IPython supports the Python language and Jupyter is language-agnostic. Jupyter provides the following features:

  • An interactive shell for OS commands

  • A Qt console for interactive shell-based analytics

  • A browser-based notebook for interactive analytics on a web browser

  • Kernels for different languages such as Python, Ruby, Julia, R, and so on

  • The nbconvert tool to convert .ipynb to other formats such as .html, .pdf, .markdown, and...

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