A CONTINUOUS DATA INTEGRATION METHODOLOGY FOR SUPPORTING REAL-TIME DATA WAREHOUSING

2007 ◽  
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Data Warehousing ◽  
Continuous Data ◽  
Time Data ◽  
Real Time Data

A rewrite/merge approach for supporting real-time data warehousing via lightweight data integration

2018 ◽  
Vol 76 (5) ◽  
pp. 3898-3922 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Nickerson Ferreira ◽  
Pedro Furtado
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Data Warehousing ◽  
Time Data ◽  
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

Big Data Management in the Context of Real-Time Data Warehousing

2013 ◽  
pp. 150-176
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Real Time ◽  
Real Life ◽  
Skewed Distribution ◽  
Stream Data ◽  
Time Data ◽  
Master Data ◽  
Real Time Data ◽  
Resource Aware

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.

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