Optimizing Latency Issues in Real-time Streaming Data in Big Data using Spark Stream Processing

EasyChair Preprint 2083

Abstract

Real-time data streaming is the process by which big volumes of data are processed quickly such that a firm extracting the info from that data can react to changing conditions in real time. Real-time streaming data is used in E-commerce, Network monitoring, Risk management, Fraud detection, Pricing and analytics. Apache Spark has become one of the most popular open source frameworks in the world also known as key cluster-computing frameworks. Spark is deployed in many ways like in Machine Learning, streaming data, and graph processing. Stream processing means each time we will process each data is processed when it arrives.

Keyphrases: Apache Spark, Big Data, data, stream processing

@booklet{EasyChair:2083,
  author    = {Karan Shah and Kalpana Mudaliar},
  title     = {Optimizing Latency Issues in Real-time Streaming Data in Big Data using Spark Stream Processing},
  howpublished = {EasyChair Preprint 2083},
  year      = {EasyChair, 2019}}