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	<title>CloverETL&#039;s Blog on Data Integration &#187; data warehousing</title>
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	<description>CloverETL tips and advice from data integration experts</description>
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		<title>Data Profiling with CloverETL Profiler beta</title>
		<link>http://blog.cloveretl.com/data-profiling-with-cloveretl-profiler-beta?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=data-profiling-with-cloveretl-profiler-beta</link>
		<comments>http://blog.cloveretl.com/data-profiling-with-cloveretl-profiler-beta#comments</comments>
		<pubDate>Mon, 31 Oct 2011 09:57:28 +0000</pubDate>
		<dc:creator>Jakub Lehotsky</dc:creator>
				<category><![CDATA[Using CloverETL]]></category>
		<category><![CDATA[CloverETL Profiler]]></category>
		<category><![CDATA[data profiling]]></category>
		<category><![CDATA[data quality]]></category>
		<category><![CDATA[data warehousing]]></category>

		<guid isPermaLink="false">http://blog.cloveretl.com/?p=1323</guid>
		<description><![CDATA[The process of data integration, data migration, consolidation and other data manipulation projects consists of a variety of steps and tasks. Javlin supports many critical tasks within these projects with a versatile ETL tool that provides technical solutions to transform &#8230; <a href="http://blog.cloveretl.com/data-profiling-with-cloveretl-profiler-beta">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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		<slash:comments>1</slash:comments>
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		<title>Loading Data Warehouse &#8211; Fact Table Load Wizard</title>
		<link>http://blog.cloveretl.com/loading-data-warehouse-with-fact-table-load-wizard?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=loading-data-warehouse-with-fact-table-load-wizard</link>
		<comments>http://blog.cloveretl.com/loading-data-warehouse-with-fact-table-load-wizard#comments</comments>
		<pubDate>Tue, 24 Aug 2010 15:46:20 +0000</pubDate>
		<dc:creator>Viliam Wallace</dc:creator>
				<category><![CDATA[Using CloverETL]]></category>
		<category><![CDATA[data warehousing]]></category>
		<category><![CDATA[ETL]]></category>

		<guid isPermaLink="false">http://blog.cloveretl.com/?p=792</guid>
		<description><![CDATA[Users who are using CloverETL for data-warehousing often need to create a data transformation which populates DWH fact table with transaction data from source system. Each such transformation contains many components that join keys from dimensional tables to processing records. &#8230; <a href="http://blog.cloveretl.com/loading-data-warehouse-with-fact-table-load-wizard">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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		<slash:comments>0</slash:comments>
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		<title>Building Data Warehouse with CloverETL: Slowly Changing Dimension Type 2</title>
		<link>http://blog.cloveretl.com/building-data-warehouse-scd2?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=building-data-warehouse-scd2</link>
		<comments>http://blog.cloveretl.com/building-data-warehouse-scd2#comments</comments>
		<pubDate>Thu, 27 May 2010 01:39:40 +0000</pubDate>
		<dc:creator>Petr Uher</dc:creator>
				<category><![CDATA[Using CloverETL]]></category>
		<category><![CDATA[CloverETL]]></category>
		<category><![CDATA[data warehousing]]></category>
		<category><![CDATA[ETL]]></category>
		<category><![CDATA[scd2]]></category>
		<category><![CDATA[slowly changing dimension]]></category>

		<guid isPermaLink="false">http://blog.cloveretl.com/?p=668</guid>
		<description><![CDATA[In the last part of our data warehouse (DWH) tutorial, I showed you how to load a dimension table that stores historical data according to the Slowly Changing Dimension Type 1 (SCD1). In today’s post, I will focus on a &#8230; <a href="http://blog.cloveretl.com/building-data-warehouse-scd2">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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		<slash:comments>0</slash:comments>
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		<title>Data Quality at a Glance Conference</title>
		<link>http://blog.cloveretl.com/data-quality-at-a-glance-conference?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=data-quality-at-a-glance-conference</link>
		<comments>http://blog.cloveretl.com/data-quality-at-a-glance-conference#comments</comments>
		<pubDate>Wed, 21 Apr 2010 16:10:14 +0000</pubDate>
		<dc:creator>Lucie Felixova</dc:creator>
				<category><![CDATA[Others]]></category>
		<category><![CDATA[data cleansing]]></category>
		<category><![CDATA[data quality]]></category>
		<category><![CDATA[data warehousing]]></category>
		<category><![CDATA[MDM]]></category>

		<guid isPermaLink="false">http://blog.cloveretl.com/?p=615</guid>
		<description><![CDATA[Javlin a.s., producer of CloverETL, took part in a Data Quality at a Glance Conference held on April 20th, at PriceWaterhouseCoopers&#8217;s premises in Prague. This conference was organized by IDG and Javlin served there as the professional supervisor partner. Javlin &#8230; <a href="http://blog.cloveretl.com/data-quality-at-a-glance-conference">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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		<slash:comments>1</slash:comments>
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		<title>Building Data Warehouse with CloverETL: Slowly Changing Dimension Type 1</title>
		<link>http://blog.cloveretl.com/building-data-warehouse-scd1?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=building-data-warehouse-scd1</link>
		<comments>http://blog.cloveretl.com/building-data-warehouse-scd1#comments</comments>
		<pubDate>Thu, 08 Oct 2009 10:21:56 +0000</pubDate>
		<dc:creator>Petr Uher</dc:creator>
				<category><![CDATA[Using CloverETL]]></category>
		<category><![CDATA[CloverETL]]></category>
		<category><![CDATA[data warehousing]]></category>
		<category><![CDATA[ETL]]></category>
		<category><![CDATA[SCD1]]></category>
		<category><![CDATA[slowly changing dimension]]></category>

		<guid isPermaLink="false">http://blog.cloveretl.com/?p=213</guid>
		<description><![CDATA[The very typical usage of ETL tools is loading the data warehouse (DWH). So I decided to write a tutorial that will describe typical data warehouse tasks (slowly changing dimensions, date dimension, filling fact tables) and propose solutions with using &#8230; <a href="http://blog.cloveretl.com/building-data-warehouse-scd1">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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		<slash:comments>1</slash:comments>
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		<title>Partitioning output records into m excel files with n sheets</title>
		<link>http://blog.cloveretl.com/partitioning-output-records-into-m-excel-files-with-n-sheets?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=partitioning-output-records-into-m-excel-files-with-n-sheets</link>
		<comments>http://blog.cloveretl.com/partitioning-output-records-into-m-excel-files-with-n-sheets#comments</comments>
		<pubDate>Thu, 02 Apr 2009 12:14:46 +0000</pubDate>
		<dc:creator>Vaclav Matous</dc:creator>
				<category><![CDATA[Using CloverETL]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[data transformation]]></category>
		<category><![CDATA[data warehousing]]></category>
		<category><![CDATA[Excel]]></category>
		<category><![CDATA[XLS]]></category>

		<guid isPermaLink="false">http://cloveretl.wordpress.com/?p=21</guid>
		<description><![CDATA[Customers often tend to have obscure requirements. In a recent project we faced an interesting issue. Output records had to be split into unknown number of excel files according to their category. In addition, records within each file should have &#8230; <a href="http://blog.cloveretl.com/partitioning-output-records-into-m-excel-files-with-n-sheets">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
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