Hadoop-Based Big Data Distributions: A Comparative Study

被引：2
作者：
Hamdaoui, Ikram [1 ]
El Fissaoui, Mohamed [2 ]
El Makkaoui, Khalid [1 ]
El Allali, Zakaria [1 ]
机构：
[1] Mohammed First Univ, FPD, MSC team, LaMAO Lab, Nador, Morocco
[2] Mohammed First Univ, FPD, LMASI Lab, Nador, Morocco
来源：
EMERGING TRENDS IN INTELLIGENT SYSTEMS & NETWORK SECURITY | 2023年 / 147卷
关键词：
Batch processing; Big data; Cloud computing; Hadoop distributions; Stream processing;
D O I：
10.1007/978-3-031-15191-0_24
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Approximately 2.5 quintillion bytes of various forms (structured, semi- structured, or unstructured) of data are generated every day. Indeed, big data technology has come to solve the limitations of traditional methods, which can no longer handle and process large amounts of data in various forms. Hadoop is an open-source big data solution created to store, process, and manage a huge volume of different types of data. Many companies developed their own Hadoop distributions based on the Hadoop ecosystem in the last decade. This paper presents the most popular Hadoop distributions, including MapR, Hortonworks, Cloudera, IBM InfoSphere BigInsights, Amazon Elastic MapReduce, Azure HDInsights, Pivotal HD, and Qubole. Then it provides readers with a deep, detailed comparison of these distributions.
引用
收藏
页码：242 / 252
页数：11
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