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You can use Oracle s table functions to perform efficient data transformations Table functions produce a collection of transformed rows that can be queried just like a regular table s data Oracle table functions are an excellent example of Oracle s sophisticated transform-while-loading paradigm Table functions can take a set of rows as input and return a transformed set of rows When you query a table function in a statement, the function returns a collection type instance representing the rows in a table The collection types can be either a VARRAY or a nested table Table functions allow you to use PL/SQL, C, or Java with SQL without any problems Table functions make the traditional use of staging tables redundant You don t need to create any intermediate tables to perform data transformations before loading data into the final data warehouse tables.

If you rely on a default date format and it changes, your application may be negatively affected It might raise an error back to the end user if the date cannot be converted, have a serious security flaw or, as bad, it might silently insert the wrong data Consider the follow INSERT statement, which relies on a default date mask: Insert into t ( date_column ) values ( '01/02/03' ); Suppose the application was relying on a default date mask of DD/MM/YY to be in place That would be February 1, 2003 (assuming that code was executed after the year 2000, but we ll visit the implications of that in a moment) Now, say someone decides the correct date format should be MM/DD/YY All of a sudden, that previous date changes to January 2, 2003 Or someone decides YY/MM/DD is right, and now you have February 3, 2001.

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Three features make table functions a powerful means of performing fast transformation of data sets: Streaming: This refers to the direct transmission of results from one process to the other without any intermediate steps The way in which a table function orders or clusters rows that it fetches from cursor arguments is called data streaming Parallel execution: This refers to the concurrent execution of the functions on multiprocessor systems Pipelining: This technique lets you see the results of a query iteratively, instead of waiting for the entire result set to be batched and returned Pipelining can thus help table functions reduce the response time by sending results as soon as they are produced in batches You also have the option of having the table function immediately return rows from a collection by using pipelining.

One caveat with regard to the TIMESTAMP WITH LOCAL TIME ZONE type is that once you create tables with this column, you will find your database s time zone is frozen, and you will not be able to change it: ops$tkyte@ORA10G> alter database set time_zone = 'PST'; alter database set time_zone = 'PST' * ERROR at line 1: ORA-30079: cannot alter database timezone when database has TIMESTAMP WITH LOCAL TIME ZONE columns.

In short, without a date format to accompany that date string, there are many ways to interpret it That INSERT statement should be: Insert into t ( date_column ) values ( to_date( '01/02/03', 'DD/MM/YY' ) ); And if you want my opinion, it has to be: Insert into t ( date_column ) values ( to_date( '01/02/2003', 'DD/MM/YYYY' ) ); That is, it must use a four-character year Not too long ago, our industry learned the hard way how much time and effort was spent remedying software that attempted to save 2 bytes We seem to have lost that lesson over time There is no excuse in the year 2010 and beyond not to use a four-character year! Just because the year 2000 has come and gone does not mean you can now use a 2 character year Think about birth dates, for example.

The elimination of (sometimes multiple) staging tables and the lack of need for any manual coding of parallel processing makes the pipelined parallel processing provided by table functions very attractive during large-scale data loading and transformation..

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