Spark Cookbook

Over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries

About This Book

• Become an expert at graph processing using GraphX
• Use Apache Spark as your single big data compute platform and master its libraries
• Learn with recipes that can be run on a single machine as well as on a production cluster of thousands of machines

Who This Book Is For

If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.

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

In Detail

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
Chapter 2: Developing Applications with Spark
Chapter 3: External Data Sources
Chapter 4: Spark SQL
Chapter 5: Spark Streaming
Chapter 6: Getting Started with Machine Learning using MLlib
Chapter 7: Supervised Learning with MLlib Regression
Chapter 8: Supervised Learning with MLlib – Classification
Chapter 9: Unsupervised Learning
Chapter 10: Recommender Systems
Chapter 11: Graph Processing Using GraphX
Chapter 12: Optimizations and Performance Tuning