Data cleaning is a self explainatory term. Most of the data warehouses in the world source data from multiple systems - systems that were created long before data warehousing was well understood, and hence without the vision to consolidate the same in a single repository of information. In such a scenario, the possiblities of the following are there:
-> Missing information for a column from one of the data sources;
-> Inconsistent information among different data sources;
-> Orphan records;
->Outliar data points;
->Different data types for the same information among various data sources, leading to improper conversion;
->Data breaching business rules
In order to ensure that the data warehouse is not infected by any of these discrepencies, it is important to cleanse the data using a set of business rules, before it makes its way into the data warehouse.

In DWH loops may exist between the table. If loops exist, then query generation will take more time, because more than one path is available. It creates ambiguity also. Loops can be avoided by creating aliases of the table or by context.. i.e. defining execution path.

Define the variables in Start script and use them in graph. All enviroment variables which have been exported in the shell which is invoking abinitio (  eg. all variables exported in .profile ) will also be available in Abinitio graph.

When we extract the source qualifier haing one option called blob by utilizing that option we can load to target and check in ports mainly.

Subject Area is place where  we draw the entities and attributes of the dimensions and fact table with relationships in ERWIN.Dimension modelling is brokent into subject Areas which are nothing but functional areas of an enterprise.