Fast and Interactive Analytics over Hadoop Data with Spark

By Matei A. Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, J. McCauley, M. Franklin, Scott Shenker, Ion Stoica. 2012

185 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. M. Mohammadi
2. Ala Al-Fuqaha
3. Sameh Sorour
4. Mohsen Guizani
5. Li Zhou
6. Kai Yu
7. Aaron Q. Li
8. Zichao Yang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “ick actions to achieve their target. Several researchers have proposed approaches and frameworks for fast streaming data analytics that leverage the capabilities of cloud infrastructures and services [8], [9]. However, for the aforementioned IoT applications among others, we need fast analytics in s”
• Parameter Server for Distributed Machine Learning (2013, methodology): “While Spark is substantially superior to Hadoop MapReduce due to its preservation of state and optimized execution strategy, both of these approaches use a synchronous iterative communication pattern.”
• Communication Efficient Distributed Machine Learning with the Parameter Server (2014, methodology): “For example, Mahout [5], based on Hadoop [13] and MLI [30], based on Spark [37], both adopt the iterative MapReduce framework [11].”
• Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, unknown): “Big Data platforms such as Apache Hadoop and Spark are capable to processing massive amounts of data, and they have been widely used in academia and industry [12], [13].”
• Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “Mahout [12], based on Hadoop [53] and MLI [124], based on Spark [153], both adopt the iterative MapReduce [41] framework.”
• Scaling Distributed Machine Learning with the Parameter Server (2014, unknown): “A key insight of Spark and MLI is preserving state between iterations, which is a core goal of the parameter server.”