Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
About the Author
About the Reviewers

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...