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The switch statement selects a particular case based on the value of a control expression. (You would plug this switch statement into the if (foundIt == false) statement block just before setting messageString, and change the messageString statement to use keyString.) In this case, the control expression is pressedKeys[i], which is the value of the key you ve just discovered was pressed. Depending on this value, the code sets a string called keyString to the appropriate text. Once the string has been set, the code uses the C# break keyword, causing the program to exit from the switch statement. You ve seen break before when you used it to exit from a for loop. You can also use it to exit from a switch statement.

The fact is that values in a data warehouse are not changing as dynamically as they would in a production database, so storing the values redundantly is less important than is retrieving the values as quickly as possible for a report Consequently, in many data warehouses, all the attributes for a dimension are stored in a single dimension table even if that means that categories and years are stored redundantly many times Storing redundant values in a single table is called denormalizing the data The concept is that dimension tables are relatively small (compared to the fact tables), and that performing a single join to find out the Year and the Category is much faster with only a couple of joins, so denormalizing is worth doing.

If the value in the control expression does not match any of the cases, the statement has no effect. The switch statement does not make anything possible that you couldn t do with a large number of if statements, but it does make programming easier in some situations. If you use the previous code to decode your keys, you ll have a usable text reader, but at the moment the code doesn t use the Shift keys properly, so every letter that is typed will be uppercase. However, it turns out that you can easily fix this by adding the following code after your switch statement:

Storing all the attributes for each dimension in a single denormalized dimension table produces what is called a star schema, because you end up with a single fact table surrounded by a single table for each dimension, and the result looks a bit like a star Normalizing each of the dimension tables so that there are many joins for each dimension results in a snowflake schema, because the points of the star get broken up into little branches that look like a snowflake In reality, it isn t the database that is star or snowflake, because one dimension might be fully normalized (ie, a snowflake), while another dimension in the same data warehouse might be fully denormalized (ie, a star) In fact, even within a single dimension, some attributes might be normalized into a snowflake while others are denormalized into a star.

CC CONTINUE CORAL DDL DEBUG DELETE DIRECTORY EDIT Errors FDL HELP INQUIRE LIBRARY LOGOUT MCR MON ITOR OPEN PATCH PRINT Queues REPLY RTL SEARCH SORT SPSS START SymboL Assign TECO TYPE WRITE

If you are creating a data warehouse for the purpose of creating reports directly from a relational database, the more snowflaking you do with attributes, the slower the query that populates the report will run If, however, you will use the warehouse primarily as a data source for Analysis Services, then the difference between star or snowflake dimension attributes is much less significant, and you can use other reasons (such as which database structure is easier to create and update) as the basis for a design decision..

if (keyState.IsKeyUp(Keys.LeftShift) && keyState.IsKeyUp(Keys.RightShift)) { keyString = keyString.ToLower(); }

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