Anterior
发表于 2025-3-23 11:36:07
SQL Server Source Patterns,flow execution, and scripting. The second section focuses on the data flow area of SQL Server Integration Services. This and the following chapters will discuss source, transformation, and logging patterns in the pipeline area of an Integration Services package.
吸气
发表于 2025-3-23 15:41:01
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Conducive
发表于 2025-3-23 21:57:55
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tenuous
发表于 2025-3-24 01:34:57
XML Patterns,metimes be tricky to get your data to fit into the tabular format that the SSIS data flow expects. This chapter describes the formats that work best with the XML Source and two alternative patterns for reading XML data with SSIS.
烦人
发表于 2025-3-24 04:58:01
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使乳化
发表于 2025-3-24 10:29:40
Data Warehouse Patterns,d even business principals in many different scenarios. Sometimes it’s a dump truck, used for the wholesale movement of enormous amounts of data. Other times it’s more like a scalpel, carving out with precision just the right amount of data.
irreducible
发表于 2025-3-24 12:49:21
Slowly Changing Dimensions,rm, which provides a wizard that outputs a set of transforms you need to handle the multiple steps of processing SCDs. Although the built-in SCD transform can be useful, it is not ideal for all data loading scenarios. This chapter describes how to make the most of the SCD transform and provides a couple of alternative patterns you can use.
狗窝
发表于 2025-3-24 18:51:26
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裂隙
发表于 2025-3-24 22:38:55
Deployment, can now target two different deployment models – the Package Deployment Model, which is similar to what was used in previous versions of the product, and the Project Deployment Model, which was designed for the new SSIS Catalog.
preeclampsia
发表于 2025-3-25 01:58:43
Data Correction with Data Quality Services,Data Quality Services (DQS) was first introduced in SQL Server 2012. It provides data correction and data deduplication functionality—key components for most . (ETL) processes. This chapter describes how DQS integrates with SSIS and provides patterns that enable you to achieve reliable, low-effort data cleansing within your ETL packages.