لخّصلي

First, you clean the data extracted from each source.Semantic standardization is another major task.Cleaning may just be correction of misspelling or may include resolution of conflicts between state codes and zip codes in the source data, or may deal with providing default values for missing data elements, or elimination of duplicates when you bring in the same data from multipte source systems.You standardize the data types and field lengths for same data elements retrieved from the various sources.Data transformation involves many forms of combining pieces of data from the different- sources.You resolve synonyms and homonyms.

First, you clean the data extracted from each source. Cleaning may just be correction of misspelling
or may include resolution of conflicts between state codes and zip codes in the
source data, or may deal with providing default values for missing data elements, or elimination
of duplicates when you bring in the same data from multipte source systems.
Second Standardization of data elements forms a large part of data transformation. You standardize
the data types and field lengths for same data elements retrieved from the various
sources. Semantic standardization is another major task. You resolve synonyms and
homonyms. When two or more terms from different source systems mean the same thing,
you resolve the synonyms. When a single term means many different things in different_
source systems, you resolve the homonym.
Data transformation involves many forms of combining pieces of data from the different-
sources. You combine data from a single source record or related data elements from
many source records. On the other hand, data transformation also involves purging source
data that is not useful and separating out source records into new combinations. Sorting
nd merging of data takes place on a large scale in the data staging area.