Database - Data Warehousing - Discussion

Data from different sources(databases) are extracted first and kept in staging area where all the cleansing and transformation processes are done. This may include converting the units of values, converting data to single consistent format or removing incorrect data etc.

Data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated. An organization in a data-intensive field like banking, insurance, retailing, telecommunications, or transportation might use a data scrubbing tool to systematically examine data for flaws by using rules, algorithms, and look-up tables.

The data scrubbing is some time called as data cleaning and it is the process of correcting and detecting errors in data base because of the software application like using in bank some where it clean up the database (in a duplicate copy in sophisticated software application).

Data scrubbing is otherwise called data cleaning. It is used for periodical error correcting. This helps in improving the quality of data.

Data scrubbing means data cleaning of data in database which is incorrect, incomplete or duplicated.

Data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated.

Scrub -- one of the steps taken in the transform stage, where the data is cleansed to detect inaccurate records and make it consistent for dwh use.

Because in scrubbing you can add the colomns in target which are not available in source by applying calculations on existing source data.

Source table: emp(empno, ename, job, sal)

Eg. Calculate Tax = Sal*.12 and add column tax in target

Target table: emp_tax(empno,ename.job,sal,tax)