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fueled by the fear, greed, and hope of millions of market participants So it should not be surprising that the market indicators in this chapter are used to monitor what the crowd is feeling These indicators are perhaps the easiest to read and understand, but they can give you the most revealing clues, especially at market extremes Knowing when the crowds are panicked or overconfident is essential if you are going to enter the market The stock market is psychological warfare, and you d better know what others are thinking before you enter The following indicators are commonly referred to as sentiment indicators because they monitor the sentiment, or psychology, of the market And as you ll soon find out, it s an Alice in Wonderland kind of world, where up is down and down is up Traders and investors closely follow the first indicator, the AAII Sentiment Survey

For example, the following statement saves the active workbook in Excel 2007/2008 format in the folder Shared:Spreads:Excel with the filename Production Statisticsxlsx, setting Excel to create a backup:

The Data Profiling task output can be used to make decisions about the quality of your data, and by incorporating the task output into your ETL process, you can automate these decisions. By taking things a little further, you can make these decisions self-adjusting as your data changes over time. We ll take a look at both scenarios in the following sections.

save workbook as active workbook filename "Shared:Spreads:Excel:Production Statisticsxlsx" file format Excel XML file format with create backup

Most commonly, the output of the Data Profiling task will change the flow of your ETL depending on the quality of the data being processed in your ETL. A simple example of this might be using the Column Null Ratio profile to evaluate a Customer table prior to extracting it from the source system. If the null ratio is greater than 30 percent for the Customer Name column, you might have your SSIS package set up to abort the processing and log an error message. This is an example of using data profiling information to prevent bad data from entering your data warehouse. In situations like the preceding, though, a large percentage of rows that may have had acceptable data quality would also be excluded. For many data warehouses, that s not acceptable. It s more likely that these hard rules, such as not allowing null values in certain columns, will be implemented on a row-by-row basis, so that all acceptable data will be loaded into the warehouse, and only bad data will be excluded. In SSIS, this is often accomplished in the data flow by using Conditional Split transformations to send invalid data to error tables.

Name: American Association of Individual Investors (AAII) Where to find: wwwaaiicom/sentimentsurvey, Barron s, Forbes, and other financial periodicals

A more complex example involves using data profiling to establish what good data looks like, and then using this information to identify data of questionable quality. For example, if you are a retailer of products from multiple manufactures, your Product table will likely have the manufacturer s original part number, and each manufacturer may have its own format for part numbers. In this scenario, you might use the Column Pattern profile against a known good source of part numbers, such as your Product table or your Product master, to identify the regular expressions that match the part numbers. During the execution of your ETL process, you could compare new incoming part numbers with these regular expressions to determine if they match the

The following statement saves the workbook referenced by the variable this_book in Excel 97 2004 format in the folder Shared:Spreads:Excel with the filename Recruitment Tacticsxls, explicitly preventing the workbook from being added to the Recent Documents list: