tailieunhanh - Microsoft SQL Server 2008 R2 Unleashed- P215

Microsoft SQL Server 2008 R2 Unleashed- P215:SQL Server 2005 provided a number of significant new features and enhancements over what was available in SQL Server 2000. This is not too surprising considering there was a five-year gap between these major SQL Server 2008 is not as much of a quantum leap forward from SQL Server 2005 | 2104 CHAPTER 52 SQL Server Integration Services Your bulk data movement doesn t have to be faster than the speed of light. Unfortunately SSIS must utilize conventional connection techniques to these data sources. It must also create intermediate buffers to hold data during the transformation steps. This usually disqualifies SSIS on the high-performance side of requirements at least for large bulk data movements with any type of data transformations defined . However many performance enhancements are present in SSIS and the data providers that are now supported which has resulted in about a 50 increase in bulk data movement speeds. Alternative importing exporting facilities such as bcp offer better performance but lack the flexibility of SSIS. The following additional SSIS data sources and destinations are supported An XML source for extracting data from XML documents directly Full insert and updating support for SQL Server Mobile destinations Reading and writing to Raw data files sources and destinations Creating an in-memory ADO DB recordset via a destination Direct access to a number of Analysis Services object destinations for example mining models cubes and dimensions The DataReader source and destination for reading and writing to any .NET framework data provider SQL Server 2008 now supports the following additional SSIS data transformations Data warehousing operations such as the Aggregate Pivot Un-pivot and Slowly Changing Dimension transformations Enhanced text data mining via the Term Extraction and Term Lookup transformations Caching for Lookup transformations Enhancing data values from a lookup table via the Data Lookup and Fuzzy Lookup transformations The identification of similar data rows via the Fuzzy Grouping transformation Multiple downstream data flow component data distribution via the Conditional Split and Multicast transformations The merging and combining of data rows from multiple upstream data flow components via the Union All Merge

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