As your business has grown so have your data sources. ERP, CRM, SFA, EDI - with all these data sources to keep track of how do you know which data is accurate? How do you transfer data from one system to another? These questions drive the need for an affordable, easy to deploy and expandable system that allows small and mid-sized companies to automate the process of transferring data lowering operational costs and ensuring consistency of data throughout the organization.
UNITE! Free Trial
Case Studies Call Now
BI Workshop Call Now
The Data e-Book Call
Data Transformation
Data transformation is a term that is used to describe the conversion
of data from a source into a destination. Destination data applies
to meta data (data about the 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 may require one
to many and many to one transformation rules.
Data Transformation Steps
Data transformation generally takes place in two steps. The
first step involves mapping of data and data elements from the source
to the destination along with any data transformation that may occur. The
second step is the code generation that will create the actual data
transformation program or map. The code generation aspect of
data transformation will actually creates an usable program that can
be installed on a computer system. In addition, during the code
generation portion, computer languages that are easy to maintain can
be created.
Data Transformation and XML
Data transformation can be done in a few different 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 purpose, cost, transformation level of value. Two of the
more popular data transformation languages are XSLT, which is an XML
data transformation language and TXL, which is a prototype language
that is used in and for data transformation.
Data Transformation and C++
Data transformation is actually quite difficult and many people struggle
immensely with it. One of the biggest problems is with C++. In
this form of data transformation the problem usually lies with the unstructured
preprocessor directives. These are preprocessor directives that
do not have blocks of code with simple grammar descriptions, making
the transformation quite hard. When there are problems such as
this, the DMS Solutions Reengineering Toolkit is usually quite helpful.
Data Transformation and Distribution
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 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 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.
