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[share_ebook] The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleanin


作者: Ralph Kimball, Joe Caserta

日期: 2004-09-13

ISBN: 0764567578

页数: 528

语言: English

出版社: Wiley

标签: 数据库



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share_ebook The Data Warehouse ETL Toolkit Practical Techniques for Extracting Cleanin

Title:The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleanin
Author:Ralph Kimball, Joe Caserta
Publisher:Wiley
ISBN:0764567578
ISBN13:
Date2004-09-13
Pages:528
Language:English
Format: PDF
Size:12.2 MB

Description:Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality


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Comments for "[share_ebook] The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleanin":

  1. Magaroc @ 2012-06-14 07:12:21, email hidden

    We largely use ELT in large part so that we have the uenetlrad data in our database for reference if there are any problems that need troubleshooting.What I don't like about ETL is that detecting errors can be more difficult, and then fixing them can be even more difficult because this may require modifications to SSIS packages or other such things.I prefer to have the load process as dumb as possible, just load EXACTLY what is in the file into the database, change NOTHING. Then, once in the database, its easier to see the data, profile the data, make assessments and in the even of unexpected data, address it as needed instead of having largely hidden processes dealing with it.I will say though that we deal largely with custom data loads, for a data feed that is well defined and consistent, then ETL makes more sense. When loading new data, or data from new sources, etc., I prefer ELT.

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