Spark Cookbook

With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side
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Spark Cookbook

Rishi Yadav

1 customer reviews
With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side
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Book Details

ISBN 139781783987061
Paperback226 pages

Book Description

By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by up to 100 times.

This book will focus on how to analyze large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will cover setting up development environments. You will then cover various recipes to perform interactive queries using Spark SQL and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will then focus on machine learning, including supervised learning, unsupervised learning, and recommendation engine algorithms. After mastering graph processing using GraphX, you will cover various recipes for cluster optimization and troubleshooting.

Table of Contents

Chapter 1: Getting Started with Apache Spark
Introduction
Installing Spark from binaries
Building the Spark source code with Maven
Launching Spark on Amazon EC2
Deploying on a cluster in standalone mode
Deploying on a cluster with Mesos
Deploying on a cluster with YARN
Using Tachyon as an off-heap storage layer
Chapter 2: Developing Applications with Spark
Introduction
Exploring the Spark shell
Developing Spark applications in Eclipse with Maven
Developing Spark applications in Eclipse with SBT
Developing a Spark application in IntelliJ IDEA with Maven
Developing a Spark application in IntelliJ IDEA with SBT
Chapter 3: External Data Sources
Introduction
Loading data from the local filesystem
Loading data from HDFS
Loading data from HDFS using a custom InputFormat
Loading data from Amazon S3
Loading data from Apache Cassandra
Loading data from relational databases
Chapter 4: Spark SQL
Introduction
Understanding the Catalyst optimizer
Creating HiveContext
Inferring schema using case classes
Programmatically specifying the schema
Loading and saving data using 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
Chapter 5: Spark Streaming
Introduction
Word count using Streaming
Streaming Twitter data
Streaming using Kafka
Chapter 6: Getting Started with Machine Learning Using MLlib
Introduction
Creating vectors
Creating a labeled point
Creating matrices
Calculating summary statistics
Calculating correlation
Doing hypothesis testing
Creating machine learning pipelines using ML
Chapter 7: Supervised Learning with MLlib – Regression
Introduction
Using linear regression
Understanding cost function
Doing linear regression with lasso
Doing ridge regression
Chapter 8: 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 Forests
Doing classification using Gradient Boosted Trees
Doing classification with Naïve Bayes
Chapter 9: Unsupervised Learning with MLlib
Introduction
Clustering using k-means
Dimensionality reduction with principal component analysis
Dimensionality reduction with singular value decomposition
Chapter 10: Recommender Systems
Introduction
Collaborative filtering using explicit feedback
Collaborative filtering using implicit feedback
Chapter 11: Graph Processing Using GraphX
Introduction
Fundamental operations on graphs
Using PageRank
Finding connected components
Performing neighborhood aggregation
Chapter 12: Optimizations and Performance Tuning
Introduction
Optimizing memory
Using compression to improve performance
Using serialization to improve performance
Optimizing garbage collection
Optimizing the level of parallelism
Understanding the future of optimization – project Tungsten

What You Will Learn

  • Install and configure Apache Spark with various cluster managers
  • Set up development environments
  • Perform interactive queries using Spark SQL
  • Get to grips with real-time streaming analytics using Spark Streaming
  • Master supervised learning and unsupervised learning using MLlib
  • Build a recommendation engine using MLlib
  • Develop a set of common applications or project types, and solutions that solve complex big data problems
  • Use Apache Spark as your single big data compute platform and master its libraries

Authors

Table of Contents

Chapter 1: Getting Started with Apache Spark
Introduction
Installing Spark from binaries
Building the Spark source code with Maven
Launching Spark on Amazon EC2
Deploying on a cluster in standalone mode
Deploying on a cluster with Mesos
Deploying on a cluster with YARN
Using Tachyon as an off-heap storage layer
Chapter 2: Developing Applications with Spark
Introduction
Exploring the Spark shell
Developing Spark applications in Eclipse with Maven
Developing Spark applications in Eclipse with SBT
Developing a Spark application in IntelliJ IDEA with Maven
Developing a Spark application in IntelliJ IDEA with SBT
Chapter 3: External Data Sources
Introduction
Loading data from the local filesystem
Loading data from HDFS
Loading data from HDFS using a custom InputFormat
Loading data from Amazon S3
Loading data from Apache Cassandra
Loading data from relational databases
Chapter 4: Spark SQL
Introduction
Understanding the Catalyst optimizer
Creating HiveContext
Inferring schema using case classes
Programmatically specifying the schema
Loading and saving data using 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
Chapter 5: Spark Streaming
Introduction
Word count using Streaming
Streaming Twitter data
Streaming using Kafka
Chapter 6: Getting Started with Machine Learning Using MLlib
Introduction
Creating vectors
Creating a labeled point
Creating matrices
Calculating summary statistics
Calculating correlation
Doing hypothesis testing
Creating machine learning pipelines using ML
Chapter 7: Supervised Learning with MLlib – Regression
Introduction
Using linear regression
Understanding cost function
Doing linear regression with lasso
Doing ridge regression
Chapter 8: 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 Forests
Doing classification using Gradient Boosted Trees
Doing classification with Naïve Bayes
Chapter 9: Unsupervised Learning with MLlib
Introduction
Clustering using k-means
Dimensionality reduction with principal component analysis
Dimensionality reduction with singular value decomposition
Chapter 10: Recommender Systems
Introduction
Collaborative filtering using explicit feedback
Collaborative filtering using implicit feedback
Chapter 11: Graph Processing Using GraphX
Introduction
Fundamental operations on graphs
Using PageRank
Finding connected components
Performing neighborhood aggregation
Chapter 12: Optimizations and Performance Tuning
Introduction
Optimizing memory
Using compression to improve performance
Using serialization to improve performance
Optimizing garbage collection
Optimizing the level of parallelism
Understanding the future of optimization – project Tungsten

Book Details

ISBN 139781783987061
Paperback226 pages
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