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You're reading from Scala Data Analysis Cookbook

Product type Book

Published in Oct 2015

Publisher

ISBN-13 9781784396749

Pages 254 pages

Edition 1st Edition

Languages

Scala

Concepts

Data Analysis

Author (1):

Arun Manivannan

Table of Contents (14) Chapters

Scala Data Analysis Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

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

Introduction
Getting Apache Spark
Creating a DataFrame from CSV
Manipulating DataFrames
Creating a DataFrame from Scala case classes

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

Introduction
Visualizing using Zeppelin
Creating scatter plots with Bokeh-Scala
Creating a time series MultiPlot with Bokeh-Scala

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

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

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

Chapter 3. Loading and Preparing Data – DataFrame

In this chapter, we will cover the following recipes:

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

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Table of Contents (14) Chapters

Chapter 3. Loading and Preparing Data – DataFrame

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Table of Contents (14) Chapters

Chapter 3. Loading and Preparing Data – DataFrame

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