Apache Spark 2.x Cookbook

Apache Spark 2.x Cookbook

By : Rishi Yadav

Buy this Book

Apache Spark 2.x Cookbook

By: Rishi Yadav

Buy this Book

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Getting Started with Apache Spark

Introduction

Leveraging Databricks Cloud

Deploying Spark using Amazon EMR

Installing Spark from binaries

Building the Spark source code with Maven

Launching Spark on Amazon EC2

Deploying Spark on a cluster in standalone mode

Deploying Spark on a cluster with Mesos

Deploying Spark on a cluster with YARN

Understanding SparkContext and SparkSession

Understanding resilient distributed dataset - RDD

Developing Applications with Spark

Introduction

Exploring the Spark shell

Developing a Spark applications in Eclipse with Maven

Developing a Spark applications in Eclipse with SBT

Developing a Spark application in IntelliJ IDEA with Maven

Developing a Spark application in IntelliJ IDEA with SBT

Developing applications using the Zeppelin notebook

Setting up Kerberos to do authentication

Enabling Kerberos authentication for Spark

Spark SQL

Understanding the evolution of schema awareness

Understanding the Catalyst optimizer

Inferring schema using case classes

Programmatically specifying the schema

Understanding the Parquet format

Loading and saving data using the JSON format

Loading and saving data from relational databases

Loading and saving data from an arbitrary source

Understanding joins

Analyzing nested structures

Working with External Data Sources

Introduction

Loading data from the local filesystem

Loading data from HDFS

Loading data from Amazon S3

Loading data from Apache Cassandra

Spark Streaming

Introduction

WordCount using Structured Streaming

Taking a closer look at Structured Streaming

Streaming Twitter data

Streaming using Kafka

Understanding streaming challenges

Getting Started with Machine Learning

Introduction

Creating vectors

Calculating correlation

Understanding feature engineering

Understanding Spark ML

Understanding hyperparameter tuning

Supervised Learning with MLlib — Regression

Introduction

Using linear regression

Understanding the cost function

Doing linear regression with lasso

Doing ridge regression

Supervised Learning with MLlib — Classification

Introduction

Doing classification using logistic regression

Doing binary classification using SVM

Doing classification using decision trees

Doing classification using random forest

Doing classification using gradient boosted trees

Doing classification with Naïve Bayes

Unsupervised Learning

Introduction

Clustering using k-means

Dimensionality reduction with principal component analysis

Dimensionality reduction with singular value decomposition

Recommendations Using Collaborative Filtering

Introduction

Collaborative filtering using explicit feedback

Collaborative filtering using implicit feedback

Graph Processing Using GraphX and GraphFrames

Introduction

Fundamental operations on graphs

Using PageRank

Finding connected components

Performing neighborhood aggregation

Understanding GraphFrames

Optimizations and Performance Tuning

Optimizing memory

Leveraging speculation

Optimizing joins

Using compression to improve performance

Using serialization to improve performance

Optimizing the level of parallelism

Understanding project Tungsten

Customer Reviews

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Dimensionality reduction with principal component analysis

Dimensionality reduction is the process of reducing the number of dimensions or features. A lot of real data contains a very high number of features. It is not uncommon to have thousands of features. So we need to drill down to features that matter.

Dimensionality reduction serves several purposes, such as:

Data compression
Visualization

When the number of dimensions is reduced, it reduces the disk and memory footprint. Last but not least, it helps algorithms to run faster. It also helps reduce highly correlated dimensions to one.

Humans can only visualize three dimensions, but data has access to a much higher number of dimensions. Visualization can help find hidden patterns in a particular piece of data. Dimensionality reduction helps visualization by compacting multiple features into one.

The most popular algorithm for dimensionality reduction is principal component analysis (PCA).

Let's look at the following dataset:

Let's say the goal...

Apache Spark 2.x Cookbook

By : Rishi Yadav

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

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