This is very useful transform for creating Time dimension tables. It generates dates incremented as you specify.
Reverse Pivot Transform in BODS:
This transform converts rows into columns. It will group the dataset of different rows into a single row with different columns. Observe the icon, it says that will convert rows to column. Continue reading “Reverse Pivot in BODS”
Pivot in Data Services:
This creates a new row for every value that you specify as a pivot column. Observe the icon, it says that it will convert column to rows.
Options: Continue reading “Pivot in BODS”
The Row_Generation transformation is represented by the above symbol.
Definition:- Row generation produces a data set with a single column. The column values start with the number that you set in the option “Row number starts”. The value then increments by 1 to a specified number of rows. Continue reading “Row Generation in BODS”
SQL Transform: You can write your own customized SQL query here. It appears in your dataflow as a source. Use this transform when you cannot perform using other DI transformations. Whatever the SQL entered, it will not be validated by DI. Continue reading “SQL Transform in Data Integrator”
Map Operation in Data Services Designer allows conversions between data manipulation operations. Using Map_Operation transform you can change operation codes on data sets to produce the desired output. Continue reading “Map Operation in BODS”
Case Transformation is used to break a single set of data into multiple sets using a condition.
For example in a spread sheet there is data for Employee Name and Region ID. Using Case and Merge transformation, you can break the Data (Single set of Data) in to multiple sets based on the region ID and store them in separate tables. You can again merge the data from all these tables in to a single permanent table using the Merge transformation. Continue reading “Case and Merge Transformations in BODS”
An export datasource is created when you want to use an info provider to load data to another targets which may reside in the same system or different system. An export datasource is created by default for a DSO. But its not the case for cubes. Below I will discuss one such scenario where I created and transported an export datasource.
You want to load data from cube in one system to a cube in another system. Let’s say that the source system is a SAP BW system with id SRD and target system is another BW system with system id TGD
SRD – SRT – SRP (Source system landscape)
TGD – TGT – TGP (Target system landscape)
Now there is a cube in SRD system which has data. This data needs to be get loaded to a cube in TGD system based on certain filters and conditions. And this has to finally move to the production systems (SRP and TGP)
The way to do this is first to request for a TR (transport request) in both SRD and TGD systems.
Sometimes while transporting changes from Dev to Quality system or from Quality to Production system, the TR fails with RC 12 error. This is a critical error which does not depend on the contents of the TR. There may be many scenarios leading to this error, I have mentioned one of them in my earlier post, this post is about one more such cases where I encountered this error-
Root Cause Analysis-
In this case, there was table space issue in the quality system and hence while transporting the changes, incomplete objects were transported resulting in a dump in the quality system and TR failing with RC=12 error-
Below is the screenshot of the error that we got in the TR-
Below is the screen shot of the dump which we were getting in the quality system-
We asked basis team to check the tablespace in the quality system. They extended the tablespace of the table mentioned in the error log/ short dump. The TR was then moved successfully.
Hope this helps..
Transformations are there in BW to change the data based on some logic. These are also there in ETL tool like BODS. These can be used to apply any business rules or cleanse and format the data from source to target.
Query transformation is used to validate the data. There are three types of validation that can occur-Lookup, Format and Mandatory validation. Continue reading “Validation Transformations in BODS”