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SQL Server Integration Services (SSIS) - Real time Examples of Data Flow Transformations in SSIS

Posted by Karthikeyan Anbarasan Articles | SQL Server April 24, 2011
In my series of articles there are a few scenarios where we need to take care of some real-world samples and examples to help explain concepts to the end users. This is part 49 of the series of articles on SSIS.
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Introduction:


In my series of articles there are a few scenarios where we need to take care of some real-world samples and examples to help explain concepts to the end users. So I came up with this article to show some real-world scenarios for each and every transformation. There are 28 data flow transformations and I grouped them below to give some realistic idea from my working experience with those controls.
You can look into my series of articles on SSIS at the url - http://f5debug.net/all-articles/
The list of 28 Transformations are as follows:

S No

Transformation

Real Time Examples

1

Aggregate Summing / Averaging a total of the products purchased by a customer online to produce the final amount.

2

Audit For audit purpose, when we need to audit the logs to send to DBA's for weekly or monthly auditing.

3

Character Map For sending mails (do some manipulations) to the end users to do some formatting we can use this task.

4

Conditional Split Morning feeds which we get from different systems need to be transferred to different tables based on the feed which we get so we can use this task to do some condition check.

5

Copy Column Morning feeds which needs to be transferred to tables need to be scanned under for cleaning spaces, empty values etc. then we can go with this task

6

Data Conversion Daily monitoring of the input files and data to have proper datatype before mapping it to the table then we can use this task.

7

Data Mining Query Evaluating the input data against the analysis model to get a proper set.

8

Derived Column Adding a title of courtesy (Mr., Mrs., Dr, etc) before the name and removing the trailing and ending spaces.

9

Export Column When we get the normal files/pdf files/image files from different systems and save it under a particular folder and map it to the table master

10

Fuzzy Grouping Matching the name of a customer with master and child table and use it to group and get the desired set

11

Fuzzy Lookup Matching the name of a customer with master and child table and use it to group and get the desired set

12

Import Column When we get the normal files/pdf files/image files from different systems and save it under a particular folder and map it to the table master

13

Lookup Employee table information saved in a master file and the region wise data available across the table which can be mapped and joined to perform a joined querying operation

14

Merge Combine data from multiple data source like master and child employee table and get result in single dataset.

15

Merge Join Combine data from multiple data source like master and child employee table and get result in single dataset. Can use any type of join like inner, outer, left , right etc

16

Multicast Similar to the conditional split but this splits across all the parts

17

OLE DB Command Used when we need to do updates to all the rows of a table like update If a message sent to the entire customer who have made a payment today.

18

Percentage Sampling Can be used in cases like the package should have access to only limited data.

19

Pivot When data fetched from the table and do some formatting to show in the front end we can use it.

20

Row Count Any point to log the count of the number of customers so we can get the count using this

21

Row Sampling Same as Percentage Sampling.

22

Script Component Used for places where we need to use framework specific assemblies.

23

Slowly Changing Dimension When we need to use some historic dimensions of data

24

Sort To make some sorting to get the desired result. Sorting like customer who made the highest payment in a particular day.

25

Term Extraction Used to get a data from a large set of data and get the extracted output in a formatted set.

26

Term Lookup Used to get a data from a large set of data and get the extracted output in a formatted set.

27

Union All Used to get data from different data sources and get in a single dimensional format.

28

Unpivot Restructuring the format of the data for normalizing the input prior to loading.

 Conclusion:


In this article we have seen some real-world examples where we use the transformations; these are some of the realistic usages which I have came across.

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