Spark has been established as an attractive platform for big data analysis, since it manages
to hide most of the complexities related to parallelism, fault tolerance and cluster setting from …

Spark has been established as an attractive platform for big data analysis, since it manages
to hide most of the complexities related to parallelism, fault tolerance and cluster setting from …

Today's scientific applications are increasingly relying on a variety of data sources, storage
facilities, and computing infrastructures, and there is a growing demand for data analysis …

Spark has emerged as an easy to use, scalable, robust and fast system for analytics with a
rapidly growing and vibrant community of users and contributors. It is multipurpose—with …

This book is a concise and easy-to-understand tutorial for big data and Spark. It will help you
learn how to use Spark for a variety of big data analytic tasks. It covers everything that you …

Spark has been increasingly adopted by industries in recent years for big data analysis by
providing a fault tolerant, scalable and easy-to-use in memory abstraction. Moreover, the …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

Sheer increase in volume of data over the last decade has triggered research in cluster
computing frameworks that enable web enterprises to extract big insights from big data …

Spark has been increasingly employed by industries for big data analytics recently, due to its
resilience, scalability and efficient in-memory distributed programming model. Meanwhile …

Apache Spark provides a large number of configuration settings that may be tuned to
improve the performance of specific applications running on the platform. However, it is non …