Scala Data Analysis Cookbook

Scala Data Analysis Cookbook

By : Arun Manivannan

Buy this Book

Scala Data Analysis Cookbook

By: Arun Manivannan

Buy this Book

Overview of this book

This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you’ll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.

Scala Data Analysis Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Getting Started with Breeze

Introduction

Getting Breeze – the linear algebra library

Working with vectors

Working with matrices

Vectors and matrices with randomly distributed values

Reading and writing CSV files

Getting Started with Apache Spark DataFrames

Introduction

Getting Apache Spark

Creating a DataFrame from CSV

Manipulating DataFrames

Creating a DataFrame from Scala case classes

Loading and Preparing Data – DataFrame

Introduction

Loading more than 22 features into classes

Loading JSON into DataFrames

Storing data as Parquet files

Using the Avro data model in Parquet

Loading from RDBMS

Preparing data in Dataframes

Data Visualization

Introduction

Visualizing using Zeppelin

Creating scatter plots with Bokeh-Scala

Creating a time series MultiPlot with Bokeh-Scala

Learning from Data

Introduction

Supervised and unsupervised learning

Gradient descent

Predicting continuous values using linear regression

Binary classification using LogisticRegression and SVM

Binary classification using LogisticRegression with Pipeline API

Clustering using K-means

Feature reduction using principal component analysis

Scaling Up

Introduction

Building the Uber JAR

Submitting jobs to the Spark cluster (local)

Running the Spark Standalone cluster on EC2

Running the Spark Job on Mesos (local)

Running the Spark Job on YARN (local)

Going Further

Introduction

Using Spark Streaming to subscribe to a Twitter stream

Using Spark as an ETL tool

Using StreamingLogisticRegression to classify a Twitter stream using Kafka as a training stream

Using GraphX to analyze Twitter data

Index

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Introduction

In this chapter, we'll be looking at how to bundle our Spark application and deploy it on various distributed environments.

As we discussed earlier in Chapter 3, Loading and Preparing Data – DataFrame the foundation of Spark is the RDD. From a programmer's perspective, the composability of RDDs such as a regular Scala collection is a huge advantage. RDD wraps three vital (and two subsidiary) pieces of information that help in reconstruction of data. This enables fault tolerance. The other major advantage is that while the processing of RDDs could be composed into hugely complex graphs using RDD operations, the entire flow of data itself is not very difficult to reason with.

Other than optional optimization attributes, such as data location, an RDD at its core wraps only three vital pieces of information:

The dependent/parent RDD (empty if not available)
The number of partitions
The function that needs to be applied to each element of the RDD

Spark spawns one task per partition. So...

Scala Data Analysis Cookbook

By : Arun Manivannan

Scala Data Analysis Cookbook

By: Arun Manivannan

Overview of this book

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Scala Data Analysis Cookbook

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