Book description
Over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX 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.
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
Table of contents
-
Spark Cookbook
- Table of Contents
- Spark Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Getting Started with Apache Spark
- 2. Developing Applications with Spark
- 3. External Data Sources
-
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
- 5. Spark Streaming
- 6. Getting Started with Machine Learning Using MLlib
- 7. Supervised Learning with MLlib – Regression
- 8. Supervised Learning with MLlib – Classification
- 9. Unsupervised Learning with MLlib
- 10. Recommender Systems
- 11. Graph Processing Using GraphX
- 12. Optimizations and Performance Tuning
- Index
Product information
- Title: Spark Cookbook
- Author(s):
- Release date: July 2015
- Publisher(s): Packt Publishing
- ISBN: 9781783987061
You might also like
book
Apache Spark 2.x Cookbook
Over 70 recipes to help you use Apache Spark as your single big data computing platform …
book
Apache Spark for Data Science Cookbook
Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache …
book
Apache Spark Quick Start Guide
A practical guide for solving complex data processing challenges by applying the best optimizations techniques in …
book
Mastering Apache Spark 2.x - Second Edition
Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced …