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 …

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 …

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 is a popular big data framework aiming to speed up computation by caching data in
memory. Spark splits the execution of an application into multiple operations which form a …

Apache Spark is one of the most popular big data tools. Despite its popularity, there are no
studies regarding its overall usage among software developers. As such, essential …

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 …

Apache Spark provides numerous configuration settings that can be tuned to improve the
performance of specific applications running on the platform. However, due to its multi-stage …

In this era of the Internet of Things (IoT), a large number of sensor devices collect and
generate various sensing data over time. It is very essential to mine fresh information by …

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 …

In recent years distributed processing frameworks such as Apache Spark have been utilized
for running big data applications. Predicting the application's execution time has been an …