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Scala Data Analysis Cookbook (new)

By : Manivannan

4 (3)

Scala Data Analysis Cookbook (new)

4 (3)

By: Manivannan

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.

Preface

Preface

Apache Flink

Scalding

Saddle

Spire

Akka

Accord

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Breeze

1. 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

2. Getting Started with Apache Spark DataFrames

2. Getting Started with Apache Spark DataFrames

Introduction

Getting Apache Spark

Creating a DataFrame from CSV

Manipulating DataFrames

Creating a DataFrame from Scala case classes

3. Loading and Preparing Data – DataFrame

3. 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

4. Data Visualization

4. Data Visualization

Introduction

Visualizing using Zeppelin

Creating scatter plots with Bokeh-Scala

Creating a time series MultiPlot with Bokeh-Scala

5. Learning from Data

5. 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

6. Scaling Up

6. 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)

7. Going Further

7. 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

Index

Preface

JVM has become a clear winner in the race between different methods of scalable data analysis. The power of JVM, strong typing, simplicity of code, composability, and availability of highly abstracted distributed and machine learning frameworks make Scala a clear contender for the top position in large-scale data analysis. Thanks to its dynamic-looking, yet static type system, scientists and programmers coming from Python backgrounds feel at ease with Scala.

This book aims to provide easy-to-use recipes in Apache Spark, a massively scalable distributed computation framework, and Breeze, a linear algebra library on which Spark's machine learning toolkit is built. The book will also help you explore data using interactive visualizations in Apache Zeppelin.

Other than the handful of frameworks and libraries that we will see in this book, there's a host of other popular data analysis libraries and frameworks that are available for Scala. They are by no means lesser beasts, and they could actually fit our use cases well. Unfortunately, they aren't covered as part of this book.

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