Abstract We benchmarked Apache Spark with a popular parallel machine learning training
application, Distributed Stochastic Gradient Descent for Matrix Factorization [5] and …

Recently, experiment-driven machine-learning (ML) based configuration tuning for in-
memory data analytics such as Apache Spark become popular because they can achieve …

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 …

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide,
written by the creators of the open-source cluster-computing framework. With an emphasis …

Due to its speed and ease of use, Spark has become a popular tool amongst data scientists
to analyze data in various sizes. Counter-intuitively, data processing workloads in industrial …

Spark big data processing platform is heavily used in today's IT services for various critical
applications such as machine learning tasks for service recommendations or massive …

Choosing the right configuration for Spark deployed in the public cloud to ensure the
efficient running of periodic jobs is hard, because there can be a huge configuration space …

Most of the data is extracted and processed by Spark in Tencent Machine Learning Platform.
However, seldom of them use Spark MLlib, an official machine learning (ML) library on top of …

In recent years, the volume of data being collected, stored, and analyzed has exploded, in
particular in relation to the activity on the Web and mobile devices, as well as data from the …

Database and big data analytics systems such as Hadoop and Spark have a large number
of configuration parameters that control memory distribution, I/O optimization, parallelism …