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