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Introduction to the Data Profiling task
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The Data Profile Viewer lets you sort most columns in the tables that it displays, which can aid you in exploring the data. It also allows you to drill down into the detail data in the source system. This is particularly useful when you have located some bad data in the profile, because you can see the source rows that contain the data. This can be valuable if, for example, the profile shows that several customer names are unusually long. You can drill into the detail data to see all the data associated with these outlier rows. This feature does require a live connection to the source database, though, because the source data is not directly included in the data profile output. One thing to be aware of with the Data Profile Viewer: not all values it shows are directly included in the XML. It does some additional work on the data profiles before presenting them to you. For example, in many cases it calculates the percentage of rows that a specific value in the profile applies to. The raw XML for the data profile only stores the row counts, not the percentages. This means that if you want to use the XML directly, perhaps to display the information on a web page, you may need to calculate some values manually. This is usually a straightforward task.
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Constraints of the Data Profiling task
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As useful as the Data Profiling task is, there are still some constraints that you need to keep in mind when using it. The first one most people encounter is in the types of data sources it will work with. The Data Profiling task requires that the data to be profiled be in SQL Server 2000 or later. This means you can t use it to directly profile data in Oracle tables, Access databases, Excel spreadsheets, or flat files. You can work around this by importing the data you need into SQL Server prior to profiling it. In fact, there are other reasons why you may want the data in SQL Server in advance, which will be touched on in this section. The Data Profiling task also requires that you use an ADO.NET connection manager. Typically, in SSIS, OLE DB connection managers are used, as they tend to perform better. This may mean creating two connection managers to the same database, if you need to both profile data and import it in the same package. Using the Data Profile Viewer does require a SQL Server installation, because the viewer is not packaged or licensed as a redistributable component. It is possible to transform the XML output into a more user-friendly format by using XSL Transformations (XSLT) to translate it into HTML, or to write your own viewer for the information. The task s performance can vary greatly, depending both on the volume of data you are profiling and on the types of profiles you have requested. Some profiles, such as the Column Pattern profile, are resource intensive and can take quite a while on a large table. One way to address this is to work with a subset of the data, rather than the entire table. It s important to get a representative sample of the data for these purposes, so that the data profile results aren t skewed. This is another reason that having the data in SQL Server can be valuable. You can copy a subset of the data to another table for profiling, using a SELECT that returns a random sampling of rows (as discussed in Selecting Rows Randomly from a Large Table from MSDN: http:/ / msdn.microsoft.com/en-us/library/cc441928.aspx). If the data is coming from an external source, such as a flat file, you can use the Row Sampling or Percentage
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