NDLI: Performance Tuning in Distributed Processing of ETL

Content Provider	IEEE Xplore Digital Library
Author	Ping Yang Zaiying Liu Jun Ni
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. Sci. & Technol., Shanghai Sanda Univ., Shanghai, China (Ping Yang; Zaiying Liu; Jun Ni)
Abstract	Extract, transform, and load (ETL) is a very common and important technology for building data warehouse includes business intelligence. When people issue a very complex SQL query to acquit data from a transaction system into a data warehouse, it involves many procedures including table-joining, sort, and aggregation. Such procedures require significant retrieving step and huge data transferring from tables. The intensive querying very often causes performance issues to be concerned. Moreover, it commonly generates negative impacts on data instance resources. How to improve the performance for ETL becomes critical and challenging. This paper presents a parallel processing solution that splitting big and complex SQL query into small pieces in distributed computing manor. The proposed method aims at reducing cost of computation, while ensuring data integrity among joined tables. The innovative idea can be verified through selected test-beds of performance tuning.
Starting Page	85
Ending Page	88
File Size	142685
Page Count	4
File Format	PDF
ISBN	9780769551180
DOI	10.1109/ICICSE.2013.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance tuning load Customer relationship management Transforms Data warehouses Educational institutions Data mining ETL Tuning Capture data changes Data extraction
Content Type	Text
Resource Type	Article

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