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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Phanikanth, K V | - |
dc.contributor.author | Sudarsan, Sithu D | - |
dc.date.accessioned | 2024-03-30T10:11:01Z | - |
dc.date.available | 2024-03-30T10:11:01Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | pp. 330-334 | en_US |
dc.identifier.isbn | 9781509025763 | - |
dc.identifier.isbn | 9781509025770 | - |
dc.identifier.uri | https://dx.doi.org/10.1109/ICACCE.2016.8073770 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14976 | - |
dc.description.abstract | Dynamic data stream processing using real time ETL techniques is currently a high concern as the amount of data generated is increasing day by day with the emergence of Internet of Things, Big Data and Cloud. Data streams are characterized by huge volume that can arrive with a high velocity and in different formats from multiple sources. Therefore, real time ETL techniques should be capable of processing the data to extract value out of it by addressing the issues related to these characteristics that are associated with data streams. In this work, we asses and analyze the capability of existing ETL techniques to handle dynamic data streams and we present whether the existing techniques are relevant in the present situation. © 2016 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Proceedings - 2016 3rd International Conference on Advances in Computing, Communication and Engineering, ICACCE 2016 | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | Big Data | en_US |
dc.subject | Continuous Query | en_US |
dc.subject | Data Streams | en_US |
dc.subject | Data Warehouse | en_US |
dc.subject | Etl | en_US |
dc.title | A Big Data Perspective of Current ETL Techniques | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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