Apache Spark for data science cookbook : overinsightful 90 recipes to get lightning-fast analytics with Apache Spark / Padma Priya Chitturi.

Format:

Book

Author/Creator:

Chitturi, Padma Priya, author.

Language:

English

Subjects (All):

Spark (Electronic resource : Apache Software Foundation).

Data mining.

Information retrieval.

Big data.

Physical Description:

1 online resource (388 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, India : Packt Publishing, 2016.

System Details:

text file

Biography/History:

Chitturi Padma Priya: Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.

Summary:

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficul...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Big Data Analytics with Spark

Introduction

Initializing SparkContext

Getting ready

How to do it…

How it works…

There's more…

See also

Working with Spark's Python and Scala shells

Building standalone applications

Working with the Spark programming model

Working with pair RDDs

Persisting RDDs

Loading and saving data

Creating broadcast variables and accumulators

Submitting applications to a cluster

Working with DataFrames

Working with Spark Streaming

Chapter 2: Tricky Statistics with Spark

Working with Pandas

Variable identification

Sampling data

Summary and descriptive statistics

Generating frequency tables.

Getting ready

Installing Pandas on Linux

Installing Pandas from source

Using IPython with PySpark

How it work…

Creating Pandas DataFrames over Spark

Splitting, slicing, sorting, filtering, and grouping DataFrames over Spark

Implementing co-variance and correlation using Pandas

Concatenating and merging operations over DataFrames

Complex operations over DataFrames

Sparkling Pandas

Chapter 3: Data Analysis with Spark

Univariate analysis

Bivariate analysis

Missing value treatment

Outlier detection

Use case - analyzing the MovieLens dataset

Use case - analyzing the Uber dataset

There's more….

See also

Chapter 4: Clustering, Classification, and Regression

Supervised learning

Unsupervised learning

Applying regression analysis for sales data

Data exploration

Feature engineering

Applying linear regression

Applying logistic regression on bank marketing data

Applying logistic regression

Real-time intrusion detection using streaming k-means

Simulating real-time data

Applying streaming k-means

Chapter 5: Working with Spark MLlib

Working with Spark ML pipelines

Implementing Naive Bayes' classification

Implementing decision trees

Building a recommendation system

How it works….

There's more…

Implementing logistic regression using Spark ML pipelines

Chapter 6: NLP with Spark

Installing NLTK on Linux

Installing Anaconda on Linux

Anaconda for cluster management

POS tagging with PySpark on an Anaconda cluster

NER with IPython over Spark

Implementing openNLP - chunker over Spark

Implementing openNLP - sentence detector over Spark

Implementing stanford NLP - lemmatization over Spark

Implementing sentiment analysis using stanford NLP over Spark

Chapter 7: Working with Sparkling Water - H2O

Features

Working with H2O on Spark

Implementing k-means using H2O over Spark

Implementing spam detection with Sparkling Water

Deep learning with airlines and weather data

Implementing a crime detection application

Running SVM with H2O over Spark

Chapter 8: Data Visualization with Spark

Visualization using Zeppelin

Installing Zeppelin

Customizing Zeppelin's server and websocket port

Visualizing data on HDFS - parameterizing inputs

Running custom functions

Adding external dependencies to Zeppelin

Pointing to an external Spark Cluster

Creating scatter plots with Bokeh-Scala

Creating a time series MultiPlot with Bokeh-Scala

Creating plots with the lightning visualization server

Visualize machine learning models with Databricks notebook

Chapter 9: Deep Learning on Spark

Installing CaffeOnSpark

Working with CaffeOnSpark

Running a feed-forward neural network with DeepLearning 4j over Spark

Running an RBM with DeepLearning4j over Spark

Running a CNN for learning MNIST with DeepLearning4j over Spark

How to do it….

How it works….

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed March 1, 2017).

OCLC:

967393459