Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You'll follow a learn-to-do-by-yourself approach to learning - learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure.

On completion, you'll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You'll also become familiar with machine learning algorithms with real-time usage.

What You Will Learn

Discover the functional programming features of Scala
Understand the complete architecture of Spark and its components
Integrate Apache Spark with Hive and Kafka
Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries
Work with different machine learning concepts and libraries using Spark's MLlib packages

Who This Book Is For

Developers and professionals who deal with batch and stream data processing.

�ber die Autorin bzw. den Autor

Subhashini Chellappan is a technology enthusiast with expertise in the big data and cloud space. She has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on business intelligence, big data analytics and cloud computing.

Dharanitharan Ganesan is a senior analyst with five years of experience in IT. He has a high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, statistical modelling and predictive analysis.

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