Spark has become a very attractive platform for big data analytics in recent years due to its
unique advantages such as parallelism, fault tolerance, and complexity associated with …

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 …

Along with the explosive growth of data, there is a great demand to speedup the ability to
process them. Although there are several platforms such as Spark that have made analysis …

Spark tuning with its dozens of parameters for performance improvement is both a challenge
and time consuming effort. Current techniques rely on trial-and-error or best guess utilizing …

Apache Spark, popularly known for big data processing capability, is a distributed open-
source platform that uses the concept of distributed memory to facilitate big data processing …

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 …

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 …

Apache Spark is an open source distributed data processing platform, which can use
distributed memory abstraction to process large volume of data efficiently. With the …

Spark is an in-memory big data analytics framework which has replaced Hadoop as the de
facto standard for processing big data in cloud platforms. These frameworks run on cloud …

For massive data processing platforms such as Spark, configuration tuning is a necessary
step since it is closely related to task parallelism, resource allocation and fault tolerance …