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Comparison of features
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At first glance, these three new features in SQL Server 2008 seem quite similar. As demonstrated here, their functionality may overlap in some cases, but they are clearly different and serve unique purposes. This treatment should help equip you with much of the information you will need to decide which feature you will need to use. To wrap up, table 1 should help you decide whether to use SQL Server Audit, change tracking, or CDC.
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Table 1 Comparing SQL Server Audit, change tracking, and change data capture Criteria Capture DML statements Capture result of DML statements Capture before and after values Capture intermediate values Capture SELECT statements Capture user name / spid Capture host name Should use specific isolation level Require SQL Server Agent Available in all SKUs SQL Server Audit Yes1 No No No Yes Yes Yes2 No No No3 Change tracking No Yes No No No Yes4 Yes4 Yes5 No Yes Change data capture No Yes Yes Yes No No No No Yes No3
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1. You can see a tokenized copy of the DML statement, but the values in the statement are replaced by parameter placeholders. 2. You can capture host name in a separate login audit event, then correlate it manually with the event in question. 3. This feature is available in Enterprise, Evaluation, and Developer Editions only. 4. You can capture this information using a trigger to affect the context information included with the change tracking data. 5. Using snapshot isolation level can significantly impact tempdb usage and performance. Additionally, this may be a concern if you use distributed transactions, change schema frequently, disable constraints when bulk loading, or take databases offline (for example, detach or auto-close). You should read up on snapshot isolation level in Books Online: http://msdn.microsoft.com/en-us/library/ ms177404(SQL.100).aspx
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SQL Server Audit, change tracking, and change data capture
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SQL Server 2008 provides a healthy offering of features that can assist you in tracking
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and dealing with changes to your data and schema. My goal for this chapter was to provide a useful and practical guide to help you decide how these features might help solve data management issues in your environment. Hopefully this will give you a good starting point on implementing one or more of these features where you need it most.
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About the author
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Aaron Bertrand is the Senior Data Architect at One to One Interactive, a global marketing agency headquartered in Boston, Massachusetts. At One to One, Aaron is responsible for database design and application architecture. Due to his commitment to the community, shown through blogging at http:/ /www.sqlblog.com, peer-to-peer support on forums and newsgroups, and speaking at user group meetings and code camps, he has been awarded as a Microsoft MVP since 1998. Aaron recently published a technical white paper for Microsoft, detailing how to use the new Resource Governor feature in SQL Server 2008.
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54 Introduction to SSAS 2008 data mining
Dejan Sarka
With SQL Server 2008, you get a complete business intelligence (BI) suite. You can use the SQL Server Database Engine to maintain a data warehouse (DW), SQL Server Reporting Services (RS) to create managed and ad hoc reports, SQL Server Integration Services (SSIS) to build and use extract, transform, and load (ETL) applications, and SQL Server Analysis Services (SSAS) to create Unified Dimensional Model (UDM) cubes. Probably the easiest step into business intelligence is using reports created with RS. But this simplicity has a price. End users have limited dynamic capabilities when they view a report. You can extend the capabilities of RS with report models, but using report models to build reports is an advanced skill for end users. You also have to consider that the performance is limited; for example, aggregating two years of sales data from a production database could take hours. Therefore, RS reports aren t useful for analyses of large quantities of data over time directly from production systems. In order to enable end users to do dynamic analysis online analytical processing (OLAP) you can implement a data warehouse and SSAS UDM cubes. In addition to dynamic change of view, end users also get lightning-speed analyses. End users can change the view of information in real time, drilling down to see more details or up to see summary information. But they re still limited with OLAP analyses. Typically, there are too many possible combinations of drilldown paths, and users don t have time to examine all possible graphs and pivot tables using all possible attributes and hierarchies. In addition, analysts are limited to searching only for patterns they anticipate. OLAP analysis is also usually limited to basic mathematical operations, such as comparing sums over different groups, operations that end users can solve graphically through client tool GUI. Data mining (DM) addresses most of these limitations. In short, data mining is data-driven analysis. When you create a DM model, you don t anticipate results in
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