Data Transformation
Data transformation is a term that s used
to describe the conversion of data from a source
data formation into destination data. Destination
data applies to meta data and the data transformation
process takes place in two basic steps. It
sounds simple in theory, but data transformation
is often quite intricate and data transformation
may require one to many and many to one transformation
rules.
More about Data Transformation
Data transformation generally takes place
in two steps. The first step of data
transformation involves data mapping maps and
data elements from the source to the destination
and capture any data transformation that may
occur. The second step of the data transformation
is the code generation that will create the
actual data transformation program. The
code generation aspect of data transformation
will actually creates an usable data transformation
program that can be installed on a computer
system. In addition, during the code
generation portion of the data transformation,
computer languages that are easy to maintain
can be created. A process that is similar
to data transformation is data mediation, but
this is different than data transformation
as a mediating data model is used.
Data transformation can be done in a few different
data transformation languages. There
are a handful of data transformation languages,
all of them having different uses and requirements
for grammar. The grammar is not unlike
Backus-Naur Form or BNF. Each of the
data transformation languages varies in its
data transformation purpose, data transformation
cost, and data transformation level of value. Two
of the more popular data transformation languages
are XSLT, which is a XML data transformation
language and TXL, which is a prototype language
that is used in and for data transformation.
Data transformation is actually quite difficult
and many people struggle immensely with data
transformation. One of the biggest data
transformation problems is with C++. In
this form of data transformation the data transformation
problem usually lies with the unstructured
preprocessor directives. These are data
transformation preprocessor directives that
do not have blocks of code with simple grammar
descriptions, making the data transformation
quite hard. When there are data transformation
problems such as this, the DMS Solutions Reengineering
Toolkit is usually quite helpful.
Data transformation is not something that is
for everyone and it is very complex based on
the data that you are trying to transform as
well as the language that is being used in the
data transformation process. The idea behind
the data transformation is to be sure that it
has a normal distribution, and this required
the need to understand transformation to linearity,
kurtosis, and skewness, all which contribute
to the normal distribution. There are many
different data transformation techniques that
are used to make sure that there is normal distribution
such as logarithm, square root, reciprocal, and
cube root. Data transformation is simply
a difficult topic that many people are never
quite able to master they way that they would
like because the grammar is hard to get just
right, as is the normal distribution. |
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