Big Data technologies: A survey
Developing Big Data applications has become increasingly important in the last few years. In fact, several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data. However, in Big Data context, traditional data techniques and platforms are less effic...
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| Veröffentlicht in: | Journal of King Saud University. Computer and information sciences Jg. 30; H. 4; S. 431 - 448 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Springer
01.10.2018
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| ISSN: | 1319-1578 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Developing Big Data applications has become increasingly important in the last few years. In fact, several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data. However, in Big Data context, traditional data techniques and platforms are less efficient. They show a slow responsiveness and lack of scalability, performance and accuracy. To face the complex Big Data challenges, much work has been carried out. As a result, various types of distributions and technologies have been developed. This paper is a review that survey recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements. It provides not only a global view of main Big Data technologies but also comparisons according to different system layers such as Data Storage Layer, Data Processing Layer, Data Querying Layer, Data Access Layer and Management Layer. It categorizes and discusses main technologies features, advantages, limits and usages. Keywords: Big Data, Hadoop, Big Data distributions, Big Data analytics, NoSQL, Machine learning |
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| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2017.06.001 |