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
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
Index

Introducing machine learning


Machine learning is the science of making machines work without programming predefined rules. Let's go through a simple example of how a program is written with a regular approach and a machine learning approach. For example, if you are developing a spam filter. You need to identify all possible parameters at design time and hardcode them within the program as follows:

spam_words = ("No investment", "Why pay more?", "You are a winner!", "Free quote")

import sys
for line in sys.stdin:
    if spam_words in line:
        print "Spam Found"
    else:
        process_lines()

In machine learning, computers will learn from the data we provide and make a decision on these spam words. Machine learning is similar to human learning. Let's understand how humans learn.

Humans learn something by doing a task over and over again, which is known as practice. Humans gain experience by practicing something. They get better at the task with more and more practice. Humans are considered...