Low quality data has
been one of the main causes of accidents and frauds in recent times. SSIS has builtin
transformations such as fuzzy lookup and fuzzy grouping that can be used to
clean, and standardize the data required by the target database. It is very important
to standardize data across an enterprise, especially when data is originating from
several subdivisions of the enterprise, where different sets of standards may be
operative, or where data is consolidated from distributed systems.
Further, the target database may have a different data type from the one that is
extracted, and in which case the data has to be modified. Data transformation or
modification can range from very simple to very complex. While changing the data
type of an extracted data to match the data type of the target database, or changing
the case of the extracted data (string) can be simple, combing several columns based
on some logic can be complex. What is nice about SSIS is that you can concatenate
several simple tasks to arrive at a complex transformation with each link providing
a specific change. As an alternative to concatenating transformations or tasks, using
the script task with the full force of the .NET framework behind it, it can produce an
optimized solution. This is indeed an awesome tool that should be put to good use.
When it comes to loading data to the target database, there is the challenging aspect
of loading the data to match the metadata of the target.
Pages:
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28