Issues and Handy Solutions Addressed at Every Stage in Real Time Data Warehousing, I.E. ETL (Extraction, Transformation & Loading) 
Arif Ali Wani1, Bansi Lal Raina2
1Arif Ali Wani, Department of Computer Science and Engineering, Glocal University, Saharanpur (U.P), India.
2Bansi Lal Raina, Department of Computer Science and Engineering, Glocal University, Saharanpur (U.P), India.
Manuscript received on 27 August 2019 | Revised Manuscript received on 03 September 2019 | Manuscript Published on 14 September 2019 | PP: 344-348 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E11000785S319/19©BEIESP | DOI: 10.35940/ijeat.E1100.0785S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the standard ETL (Extract Processing Load), the data warehouse refreshment must be performed outside of peak hours. i It implies i that the i functioning and i analysis has stopped in their iall actions. iIt causes the iamount of icleanness of i data from the idata Warehouse which iisn’t suggesting ithe latest i operational transections. This i issue is i known as i data i latency. The data warehousing is iemployed to ibe a iremedy for ithis iissue. It updates the idata warehouse iat a inear real-time iFashion, instantly after data found from the data source. Therefore, data i latency could i be reduced. Hence the near real time data warehousing was having issues which was not identified in traditional ETL. This paper claims to communicate the issues and accessible options at every point iin the i near real-time i data warehousing, i.e. i The i issues and Available alternatives iare based ion ia literature ireview by additional iStudy that ifocus ion near real-time data iwarehousing issue.
Keywords: Business Intelligence, Data Latency, Data Warehouse, Data Warehousing, ETL, Near Real Time Data Warehousing.
Scope of the Article: Real-Time Information Systems