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.

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Big Data Analytics
Venkat Ankam
Sep, 2016 | 326 pages
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Table of Contents (18 chapters)
Big Data Analytics
Big Data Analytics
Credits
Credits
About the Author
About the Author
Acknowledgement
Acknowledgement
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Big Data Analytics at a 10,000-Foot View
Big Data Analytics at a 10,000-Foot View
Big Data analytics and the role of Hadoop and Spark
Big Data science and the role of Hadoop and Spark
Tools and techniques
Real-life use cases
Summary
2
Getting Started with Apache Hadoop and Apache Spark
Getting Started with Apache Hadoop and Apache Spark
Introducing Apache Hadoop
Introducing Apache Spark
Why Hadoop plus Spark?
Installing Hadoop plus Spark clusters
Summary
3
Deep Dive into Apache Spark
Deep Dive into Apache Spark
Starting Spark daemons
Learning Spark core concepts
Lifecycle of Spark program
Spark applications
Persistence and caching
Spark resource managers – Standalone, YARN, and Mesos
Summary
4
Big Data Analytics with Spark SQL, DataFrames, and Datasets
Big Data Analytics with Spark SQL, DataFrames, and Datasets
History of Spark SQL
Architecture of Spark SQL
Introducing SQL, Datasources, DataFrame, and Dataset APIs
Evolution of DataFrames and Datasets
Why Datasets and DataFrames?
When to use RDDs, Datasets, and DataFrames?
Analytics with DataFrames
Analytics with the Dataset API
Data Sources API
Spark SQL as a distributed SQL engine
Hive on Spark
Summary
5
Real-Time Analytics with Spark Streaming and Structured Streaming
Real-Time Analytics with Spark Streaming and Structured Streaming
Introducing real-time processing
Architecture of Spark Streaming
Spark Streaming transformations and actions
Input sources and output stores
Spark Streaming with Kafka and HBase
Advanced concepts of Spark Streaming
Monitoring applications
Introducing Structured Streaming
Summary
6
Notebooks and Dataflows with Spark and Hadoop
Notebooks and Dataflows with Spark and Hadoop
Introducing web-based notebooks
Introducing Jupyter
Introducing Apache Zeppelin
The Livy REST job server and Hue Notebooks
Introducing Apache NiFi for dataflows
Summary
7
Machine Learning with Spark and Hadoop
Machine Learning with Spark and Hadoop
Introducing machine learning
Machine learning on Spark and Hadoop
Machine learning algorithms
An example of machine learning algorithms
Building machine learning pipelines
Machine learning with H2O and Spark
Introducing Hivemall
Introducing Hivemall for Spark
Summary
8
Building Recommendation Systems with Spark and Mahout
Building Recommendation Systems with Spark and Mahout
Building recommendation systems
Limitations of a recommendation system
A recommendation system with MLlib
The Mahout and Spark integration
Summary
9
Graph Analytics with GraphX
Graph Analytics with GraphX
Introducing graph processing
Getting started with GraphX
Analyzing flight data using GraphX
Introducing GraphFrames
Summary
10
Interactive Analytics with SparkR
Interactive Analytics with SparkR
Introducing R and SparkR
Getting started with SparkR
Using DataFrames with SparkR
Using SparkR with RStudio
Machine learning with SparkR
Using SparkR with Zeppelin
Summary
Index
Index

Introducing GraphFrames

While the GraphX framework is based on the RDD API, GraphFrames is an external Spark package built on top of the DataFrames API. It inherits the performance advantages of DataFrames using the catalyst optimizer. It can be used in the Java, Scala, and Python programming languages. GraphFrames provides additional functionalities over GraphX such as motif finding, DataFrame-based serialization, and graph queries. GraphX does not provide the Python API, but GraphFrames exposes the Python API as well.

It is easy to get started with GraphFrames. On a Spark 2.0 cluster, let's start a Spark shell with the packages option using the same data used to create the graph in the Creating a graph section of this chapter:

$SPARK_HOME/bin/spark-shell --packages graphframes:graphframes:0.2.0-spark2.0-s_2.11

import org.graphframes._

val vertex = spark.createDataFrame(List(
    ("1","Jacob",48),
    ("2","Jessica",45),
    ("3","Andrew",25),
    ("4","Ryan",53),
    ("5","Emily",22)...
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