A new generation of middleware solutions for a near-real-time data warehousing architecture

Author(s):  
Ronnie Abrahiem

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


2011 ◽  
pp. 279-291
Author(s):  
S.R. Balasundaram ◽  
B. Ramadoss

The rapidly changing nature of business environments requires organizations to be more flexible to gain competitive advantages. Organizations are turning into a new generation of software called Enterprise Application Integration (EAI) to fully integrate business processes. It is an activity that integrates and harmonizes an enterprise’s isolated business applications, processes and functions involving real time data. Developing quality EAI projects is quite a big challenge. Even though success of EAI projects depends on so many parameters, ‘testing’ is the most significant phase that can ensure the quality as well as the success of EAI projects. Components integrated without testing in EAI systems may affect the enterprise system as a whole. This chapter focuses on the testing aspects related to EAI applications. Especially the significance of testing for various types of “Integrations” is discussed in detail.


2021 ◽  
Author(s):  
Flavio de Assis Vilela ◽  
Ricardo Rodrigues Ciferri

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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