| Summary: | 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
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