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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.
By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools
From the team that brought you the New York Times bestseller The Spark This practical yet inspirational guide, which is based on the same easy, real-world princ
A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features Discover practical recipes for distributed d
Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Im
