Oracle discoverer vs sap business objects

BI Tool Comparision, 
Oracle Discoverer Vs SAP Business Objects 
Semantic Layer comparision
SAP BO universe Vs oracle discoverer EUL,
oracle discoverer end user layer vs sap business objects Universe,
semantic layer comparision

 

Informatica Tranformation Example

Informatica Tranformation Example  For Peoplesoft ERP HR data.
1. Informatica Expression (Source to Target) 
2. Source Qualifier transformation 
3. Expression Transformation 
4. Informatica Language example use of IFF syntax. 
5. performance optimisation using Oracle DECODE function inside Expression transformation 
6. Hexadecimal to Decimal Conversion Logic

A day in Life of ETL Consultant

Consultant Analyses the business deeper to come up with star-schema design and further ETL load design,
Working as datawarehouse consultant most important task is to fix granularity of fact across dimensions to be analysed in FACT-DIMENSION Star schema design.
Granularity depends on business requirement and key drivers for business to be analysed for having its impact on Topline and Bottomline of Company. For Clinical Research key driver is No. of patient Enrolled, For banking key driver is cost of adding new customer,
Now patient is analyzed across geography dimension, against time dimension. But at what level of Granularity.
(#no of patient, day)   OR
(#no of patient, year)  OR
(#no of patient, hour)
This depends on business need and level of criticality to time. For Stock trading Every second is crucial but not for clinical trails but if trial involve enrollment of large public it may required a drill down to per day figure in BI reports hence provisions must be there in star schema.
Besides this The Other task per day can be taken based on stage of project
https://sandyclassic.wordpress.com/2014/02/19/a-day-in-life-of-datawarehouse-architect-part-1/
For datawarehouse Engineer involved with task the day look like
https://sandyclassic.wordpress.com/2014/02/19/a-day-in-life-of-datawarehousing-engineer/
For Unstructured data analysis you can look at
https://sandyclassic.wordpress.com/2011/10/19/hadoop-its-relation-to-new-architecture-enterprise-datawarehouse/

Then data Transformation are applied for
Example in Informatica and SSIS:

https://sandyclassic.wordpress.com/2014/01/15/eaten-tv-from-partly-eaten-apple-part-2-artificial-intelligence/

Two Sets of documents are There LLD and HLD to look at what needs transformation to be applied.
Like in Informatica Transformation Types are :

http://www.techtiks.com/informatica/beginners-guide/transformations/transformation-types/

Look at all transformations available in Informatica version 9

http://www.folkstalk.com/2011/12/transformations-in-informatica-9.html

These can be customized according to logic required.
Next step is Loading to datawarehouse dimension tables  and then to Fact table.
Read: https://sandyclassic.wordpress.com/2014/02/06/coke-vs-pepsi-of-datawarehousing-etl-vs-elt/
And more

https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/

A day in life of datawarehouse Consultant

Consultant Analyses the business deeper to come up with star-schema design and further ETL load design,
Working as datawarehouse consultant most important task is to fix granularity of fact across dimensions to be analysed in FACT-DIMENSION Star schema design.
Granularity depends on business requirement and key drivers for business to be analysed for having its impact on Topline and Bottomline of Company. For Clinical Research key driver is No. of patient Enrolled, For banking key driver is cost of adding new customer,
Now patient is analyzed across geography dimension, against time dimension. But at what level of Granularity.
(#no of patient, day)   OR
(#no of patient, year)  OR
(#no of patient, hour)
This depends on business need and level of criticality to time. For Stock trading Every second is crucial but not for clinical trails but if trial involve enrollment of large public it may required a drill down to per day figure in BI reports hence provisions must be there in star schema.
Besides this The Other task per day can be taken based on stage of project
https://sandyclassic.wordpress.com/2014/02/19/a-day-in-life-of-datawarehouse-architect-part-1/
For datawarehouse Engineer involved with task the day look like
https://sandyclassic.wordpress.com/2014/02/19/a-day-in-life-of-datawarehousing-engineer/
For Unstructured data analysis you can look at
https://sandyclassic.wordpress.com/2011/10/19/hadoop-its-relation-to-new-architecture-enterprise-datawarehouse/

Then data Transformation are applied for
Example in Informatica and SSIS:

https://sandyclassic.wordpress.com/2014/01/15/eaten-tv-from-partly-eaten-apple-part-2-artificial-intelligence/

Two Sets of documents are There LLD and HLD to look at what needs transformation to be applied.
Like in Informatica Transformation Types are :

http://www.techtiks.com/informatica/beginners-guide/transformations/transformation-types/

Look at all transformations available in Informatica version 9

http://www.folkstalk.com/2011/12/transformations-in-informatica-9.html

These can be customized according to logic required.
Next step is Loading to datawarehouse dimension tables  and then to Fact table.
Read: https://sandyclassic.wordpress.com/2014/02/06/coke-vs-pepsi-of-datawarehousing-etl-vs-elt/
And more

https://sandyclassic.wordpress.com/2013/07/02/data-warehousing-business-intelligence-and-cloud-computing/

Advanced metering infrastructure Architecture

My presentation at an interview