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The SUM() will take longer than the COUNT(), particularly if the rows for January 2009 are not clustered together on the disk This may seem like an isolated example, but remember that many RDBMS products have star-join optimizers that apply dimensional constraints first and access the fact table last This query execution scheme can be applied even when the query selects one or more dimension values or when constraints are applied in more than one dimension table Not all database optimizers work this way, and those that do may offer additional mechanisms to tune or change behavior based on expected usage So, while the addition of a constant-valued fact is very useful, it pays to talk it over with your database administrator The good news is you can have it both ways Adding the additional fact does not stop anyone from writing a query that counts rows.

The basic goal was to copy all relevant information from the store locations to the server in Store 1. All data on the main computer in Store 1 would be backed up using a Zip drive a device that met the budget and data capacity requirements. After he installed the unit himself, Pop was ready to handle the details. This computer stuff isn t so difficult to understand, he explained to Lily. It s a lot like protecting our paper records in a safe. Based on that analogy (and Lily s technical input), Pop s solution involves implementing the following processes: 1. POS transactions will be copied to a floppy disk by a scheduled process several times during the day at each store. The daily sales information will easily fit on this media, and the backup can be used in case an error occurs in the database, causing a loss or corruption of data. 2. An automated dial-up process will use FTP to transfer the daily sales information from each store to the main server in Store 1. 3. A Microsoft Access macro then will import all the daily sales information from each store into a central database. If data is missing, the macro will notify Pop

COUNT() and SUM() are both available options..

The single-row INSERT statement, shown in Figure 10-1, adds a new row to a table. The INTO clause specifies the table that receives the new row (the target table), and the VALUES clause specifies the data values that the new row will contain. The column list indicates which data value goes into which column of the new row

that something has failed. In this case, he can call the appropriate store manager(s) and ask them to manually resend the files. 4. To back up the data at Store 1, Pop configured an automated process that will copy the main Microsoft Access database to a Zip drive nightly. Each week, he will take these Zip disks offsite to the bank, along with the weekly deposits. That ought to cover all the bases, Pop said, as he admired his handiwork. Mom and Lily agreed that it looked like a good system. Other than the costs for the consultant, they were all quite pleased with how things turned out.

Factless fact tables can also be used in situations that do not clearly correspond to events or activities. Some common examples include: Tracking the salesperson assigned to each customer Logging the eligibility of individuals for programs or benefits Recording when severe weather alerts are in effect Capturing the marketing campaigns that are active at a given time

For the most part, Mom, Pop, and Lily were right the solution chosen is adequate for now, and should provide for the types of data protection required At first, it might not seem like a very technical solution Notice that Pop never purchased any specific backup software, and chose a very affordable removable storage device for backup And, his home-grown data protection methods are manually implemented and are dependent on several automated processes working properly Notice, however, that Pop s methods result in what can be seen as replication over a wide area network (WAN) It might take some imagination to see at first, but in some ways, he has reinvented the wheel The difference is that Pop s wheel is much cheaper.

Suppose you just hired a new salesperson, Henry Jacobsen, with the following personal data: