A big data perspective of current ETL techniques

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 d...

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Bibliographic Details
Published in:	2016 International Conference on Advances in Computing and Communication Engineering (ICACCE) pp. 330 - 334
Main Authors:	Phanikanth, K. V., Sudarsan, Sithu D.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2016
Subjects:	Big Data continuous query Data mining data streams data warehouse Data warehouses ETL Loading Real-time systems Semantics
Online Access:	Get full text
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Summary:	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.
DOI:	10.1109/ICACCE.2016.8073770