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Home > Data > Data Analysis > Scala Data Analysis Cookbook (new)
Scala Data Analysis Cookbook (new)
Scala Data Analysis Cookbook (new)

Scala Data Analysis Cookbook (new): Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes

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eBook Oct 2015 254 pages 1st Edition
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Scala Data Analysis Cookbook (new)

Chapter 2. Getting Started with Apache Spark DataFrames

In this chapter, we will cover the following recipes:

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

Introduction

Apache Spark is a cluster computing platform that claims to run about 10 times faster than Hadoop. In general terms, we could consider it as a means to run our complex logic over massive amounts of data at a blazingly fast speed. The other good thing about Spark is that the programs that we write are much smaller than the typical MapReduce classes that we write for Hadoop. So, not only do our programs run faster but it also takes less time to write them.

Spark has four major higher level tools built on top of the Spark Core: Spark Streaming, Spark MLlib (machine learning), Spark SQL (an SQL interface for accessing the data), and GraphX (for graph processing). The Spark Core is the heart of Spark. Spark provides higher level abstractions in Scala, Java, and Python for data representation, serialization, scheduling, metrics, and so on.

At the risk of stating the obvious, a DataFrame is one of the primary data structures used in data analysis. They are just like an RDBMS table...

Getting Apache Spark

In this recipe, we'll take a look at how to bring Spark into our project (using SBT) and how Spark works internally.

Note

The code for this recipe can be found at https://github.com/arunma/ScalaDataAnalysisCookbook/blob/master/chapter1-spark-csv/build.sbt.

How to do it...

Let's now throw some Spark dependencies into our build.sbt file so that we can start playing with them in subsequent recipes. For now, we'll just focus on three of them: Spark Core, Spark SQL, and Spark MLlib. We'll take a look at a host of other Spark dependencies as we proceed further in this book:

  1. Under a brand new folder (which will be your project root), create a new file called build.sbt.
  2. Next, let's add the Spark libraries to the project dependencies.
  3. Note that Spark 1.4.x requires Scala 2.10.x. This becomes the first section of our build.sbt:
    organization := "com.packt"

name := "chapter1-spark-csv"

scalaVersion := "2.10.4"

val sparkVersion=...

Creating a DataFrame from CSV

In this recipe, we'll look at how to create a new DataFrame from a delimiter-separated values file.

Note

The code for this recipe can be found at https://github.com/arunma/ScalaDataAnalysisCookbook/blob/master/chapter1-spark-csv/src/main/scala/com/packt/scaladata/spark/csv/DataFrameCSV.scala.

How to do it...

This recipe involves four steps:

  1. Add the spark-csv support to our project.
  2. Create a Spark Config object that gives information on the environment that we are running Spark in.
  3. Create a Spark context that serves as an entry point into Spark. Then, we proceed to create an SQLContext from the Spark context.
  4. Load the CSV using the SQLContext.
  5. CSV support isn't first-class in Spark, but it is available through an external library from Databricks. So, let's go ahead and add that to our build.sbt.

    After adding the spark-csv dependency, our complete build.sbt looks like this:

    organization := "com.packt"

name := "chapter1-spark-csv"

scalaVersion...

Manipulating DataFrames

In the previous recipe, we saw how to create a DataFrame. The next natural step, after creating DataFrames, is to play with the data inside them. Other than the numerous functions that help us to do that, we also find other interesting functions that help us sample the data, print the schema of the data, and so on. We'll take a look at them one by one in this recipe.

Note

The code and the sample file for this recipe could be found at https://github.com/arunma/ScalaDataAnalysisCookbook/blob/master/chapter1-spark-csv/src/main/scala/com/packt/scaladata/spark/csv/DataFrameCSV.scala.

How to do it...

Now, let's see how we can manipulate DataFrames using the following subrecipes:

  • Printing the schema of the DataFrame
  • Sampling data in the DataFrame
  • Selecting specific columns in the DataFrame
  • Filtering data by condition
  • Sorting data in the frame
  • Renaming columns
  • Treating the DataFrame as a relational table to execute SQL queries
  • Saving the DataFrame as a file

Printing the schema...

Creating a DataFrame from Scala case classes

In this recipe, we'll see how to create a new DataFrame from Scala case classes.

Note

The code for this recipe can be found at https://github.com/arunma/ScalaDataAnalysisCookbook/blob/master/chapter1-spark-csv/src/main/scala/com/packt/scaladata/spark/csv/DataFrameFromCaseClasses.scala.

How to do it...

  1. We create a new entity called Employee with the id and name fields, like this:
    case class Employee(id:Int, name:String)

    Similar to the previous recipe, we create SparkContext and SQLContext.

    val conf = new SparkConf().setAppName("colRowDataFrame").setMaster("local[2]")

//Initialize Spark context with Spark configuration.  This is the core entry point to do anything with Spark
val sc = new SparkContext(conf)

//The easiest way to query data in Spark is to use SQL queries.
val sqlContext=new SQLContext(sc)
  2. We can source these employee objects from a variety of sources, such as an RDBMS data source, but for the sake of this example...
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Page 1 of 6

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Key benefits

  • • Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin
  • • Scale up your data anlytics infrastructure with practical recipes for Scala machine learning
  • • Recipes for every stage of the data analysis process, from reading and collecting data to distributed analytics

Description

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.

Who is this book for?

This book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis

What you will learn

  • • Familiarize and set up the Breeze and Spark libraries and use data structures
  • • Import data from a host of possible sources and create dataframes from CSV
  • • Clean, validate and transform data using Scala to pre-process numerical and string data
  • • Integrate quintessential machine learning algorithms using Scala stack
  • • Bundle and scale up Spark jobs by deploying them into a variety of cluster managers
  • • Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysis

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 30, 2015
Length: 254 pages
Edition : 1st
Language : English
ISBN-13 : 9781784394998
Vendor :
EPFL
Category :
Data
Languages :
Scala
Concepts :
Data Analysis
Tools :
Apache Spark

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Product feature icon Instant access to your Digital eBook purchase
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Product Details

Publication date : Oct 30, 2015
Length: 254 pages
Edition : 1st
Language : English
ISBN-13 : 9781784394998
Vendor :
EPFL
Category :
Data
Languages :
Scala
Concepts :
Data Analysis
Tools :
Apache Spark

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Table of Contents

8 Chapters
1. Getting Started with Breeze Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Index

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The Author has done an excellent job!
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I found the book filled with very useful and practical examples on usage of Spark. The Machine Learning and the Scaling up chapter are pretty detailed. Loved it !
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About the author

Profile icon Manivannan
Manivannan
Arun Manivannan has been an engineer in various multinational companies, tier-1 financial institutions, and start-ups, primarily focusing on developing distributed applications that manage and mine data. His languages of choice are Scala and Java, but he also meddles around with various others for kicks. He blogs at http://rerun.me. Arun holds a master's degree in software engineering from the National University of Singapore. He also holds degrees in commerce, computer applications, and HR management. His interests and education could probably be a good dataset for clustering.
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