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

Getting started with GraphX

You don't need any additional installation of software to get started with GraphX. GraphX is included within the Spark installation. This section introduces how to create and explore graphs using a simple family relationship graph. The family graph created will be used in all operations within this section.

Basic operations of GraphX

GraphX does not support the Python API yet. For easy understanding, let's use spark-shell to interactively work with GraphX. First of all, let's create input data (vertex and edge files) needed for our GraphX operations and then store it on HDFS.


All programs in this chapter are executed on CDH 5.8 VM. For other environments, file paths might change, but the concepts are the same in any environment.

Creating a graph

We can create a graph using the following steps:

  1. Create a vertex file with vertex ID, name, and age as shown here:

    [cloudera@quickstart ~]$ cat vertex.csv