Near Real-Time Data Warehousing with Multi-stage Trickle and Flip

2011 ◽  
pp. 73-82 ◽  
Author(s):  
Janis Zuters
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Time Data ◽  
Multi Stage ◽  
Real Time Data

scholarly journals Issues and Handy Solutions Addressed at Every Stage in Real Time Data Warehousing, I.E. ETL (Extraction, Transformation & Loading)

2019 ◽  
Vol 8 (5S3) ◽  
pp. 344-348
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Time Data ◽  
Processing Load ◽  
Real Time Data ◽  
Data Source

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

Bioterrorism Surveillance with Real-Time Data Warehousing

2003 ◽  
pp. 322-335 ◽  
Author(s):  
Donald J. Berndt ◽  
Alan R. Hevner ◽  
James Studnicki
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Time Data ◽  
Real Time Data

Real-Time Data Warehousing: A Rewrite/Merge Approach

2014 ◽  
pp. 78-88 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Nickerson Ferreira ◽  
Pedro Furtado
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Time Data ◽  
Real Time Data

scholarly journals An Innovative Method to Extract Data in a Real-time Data Warehousing Environment

2021 ◽  
Author(s):  
Flavio de Assis Vilela ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Data Extraction ◽  
Synthetic Data ◽  
Knowledge Discovery In Databases ◽  
Data Repository ◽  
Time Data ◽  
Innovative Method ◽  
Real Time Data ◽  
Time Requirements

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction in knowledge discovery in databases and in data warehousing environments. The ETL process aims to gather data that is available from operational sources, process and store them into an integrated data repository. Also, the ETL process can be performed in a real-time data warehousing environment and store data into a data warehouse. This paper presents a new and innovative method named Data Extraction Magnet (DEM) to perform the extraction phase of ETL process in a real-time data warehousing environment based on non-intrusive, tag and parallelism concepts. DEM has been validated on a dairy farming domain using synthetic data. The results showed a great performance gain in comparison to the traditional trigger technique and the attendance of real-time requirements.

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