Recently, we have been witnessing huge advancements in the scale of data we routinely
generate and collect in pretty much everything we do, as well as our ability to exploit modern …

Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

Apache Spark: a unified engine for big data processing Page 1 56 COMMUNICATIONS OF THE
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …

Spark SQL is a new module in Apache Spark that integrates relational processing with
Spark's functional programming API. Built on our experience with Shark, Spark SQL lets …

Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is
built on the philosophy that many classes of data processing applications, including real …

Presto is an open source distributed query engine that supports much of the SQL analytics
workload at Facebook. Presto is designed to be adaptive, flexible, and extensible. It supports …

Data is at the center of many challenges in system design today. Difficult issues need to be
figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In …

Given the well-known tradeoffs between fairness, performance, and efficiency, modern
cluster schedulers often prefer instantaneous fairness as their primary objective to ensure …

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of
application engines that are building upon the YARN resource management layer. The open …

Datacenter-scale computing for analytics workloads is increasingly common. High
operational costs force heterogeneous applications to share cluster resources for achieving …