In recent years, the volume of data being collected, stored, and analyzed has exploded, in particular in relation to the activity on the Web and mobile devices, as well as data from the physical world collected via sensor networks. While previously large-scale data storage, processing, analysis, and modeling was the domain of the largest institutions such as Google, Yahoo!, Facebook, and Twitter, increasingly, many organizations are being faced with the challenge of how to handle a massive amount of data.
With the advent of big data, extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.
The objective of this book is to get the audience the flavor of challenges in data science and addressing them with a variety of analytical tools on a distributed system such as Spark (apt for iterative algorithms), which offers in-memory processing and more flexible for data analysis at scale. This book introduces readers to the fundamentals of Spark and helps them learn the concepts with code examples. It also talks in brief about data mining, text mining, NLP, machine learning, and so on. The readers get to know how to solve real-world analytical problems with large datasets and are made aware of a very practical approach and code to use analytical tools that leverage the features of Spark.
Chapter 1, Big Data Analytics with Spark, introduces Scala, Python and R can be used for data analysis. It also details about Spark programming model, API will be introduced, shows how to install, set up a development environment for the Spark framework and run jobs in distributed mode. I will also show working with DataFrames and Streaming computation models.
Chapter 2, Tricky Statistics with Spark, shows how to apply various statistical measures such as generating sample data, constructing frequency tables, summary and descriptive statistics on large datasets using Spark and Pandas
Chapter 3, Data Analysis with Spark, details how to apply common data exploration and preparation techniques such as univariate analysis, bivariate analysis, missing values treatment, identifying the outliers and techniques for variable transformation using Spark.
Chapter 4, Clustering, Classification and Regression, deals with creating models for regression, classification and clustering as well as shows how to utilize standard performance-evaluation methodologies for the machine learning models built.
Chapter 5, Working with Spark MLlib, provides an overview of Spark MLlib and ML pipelines and presents examples for implementing Naive Bayes classification, decision trees and recommendation systems.
Chapter 6, NLP with Spark, shows how to install NLTK, Anaconda and apply NLP tasks such as POS tagging, Named Entity Recognition, Chunker, Sentence Detector, Lemmatization using Core NLP and Stanford NLP over Spark.
Chapter 7, Working with Sparkling Water - H2O, details how to integrate H2O with Spark and shows applying various algorithms such as k-means, deep learning and SVM and also show developing applications –spam detection and crime detection with Sparkling Water.
Chapter 8, Data Visualization with Spark, show the integration of widely used visualization tools such as Zeppelin, Lightning Server and highly active Scala bindings (Bokeh-Scala) for visualizing large data sets.
Chapter 9, Deep Learning on Spark, shows how to implement deep learning algorithms such as RBM, CNN for learning MNIST, Feed-forward neural networks with the tools Deep Learning4j, TensorFlow using Spark.
Chapter 10, Working with SparkR, provides examples on creating distributed data frames in R, various operations that could be applied in SparkR and details on applying user-defined functions, SQL queries and machine learning in SparkR.
Throughout this book, we assume that you have some basic experience with programming in Scala, Java, or Python and have some basic knowledge of machine learning, statistics, and data analysis.
This book is intended for entry-level to intermediate data scientists, data analysts, engineers and practitioners who want to get acquainted with solving numerous data science problems using a distributed computing framework like Spark. The readers are expected to have knowledge on statistics, data science tools like R, Pandas and understanding on distributed systems (some exposure to Hadoop).
In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Both spark-shell and PySpark are available in the bin directory of SPARK_HOME, that is, SPARK_HOME/bin"
A block of code is set as follows:
from pyspark
import SparkContext
stocks = "hdfs://namenode:9000/stocks.txt"
sc = SparkContext("<master URI>", "ApplicationName")
data = sc.textFile(stocks)
totalLines = data.count()
print("Total Lines are: %i" % (totalLines))Any command-line input or output is written as follows:
$SPARK_HOME/bin/spark-shell --master <master type>
Spark context available as sc.
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