Learning Spark: Lightning-Fast Big Data Analytics

354 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.

A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, methodology): “Apache Spark [91] [92] Real-time processing, fast processing, ability to integrate with Hadoop and existing Hadoop data In memory processing, distributed processing, suitable for real-time analytics, fault tolerant Not supported for multi-user environment, in memory processing can become the bottlen”
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017, background): “Apache Spark, a novel solution for large-scale data processing, was thought to be able to solve the Hadoop's drawbacks [32] [33] .”
Air traffic flow management under uncertainty using chance-constrained optimization (2017, methodology): “The optimization is performed on a small Spark cluster with six nodes, where Spark is a distributed computing platform ( Karau et al., 2015 ).”
Big data for cyber physical systems in industry 4.0: a survey (2019, methodology): “To avoid the high-cost I/O operation with hard-drives, the Spark system is designed to process and save data directly in computer memories instead of hard-drives (Karau et al. 2015).”
Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? (2019, background): “A very interesting option in this case is to take advantage of the features of novel environments such as Spark [68].”