Data formats and access patterns are diverse which additionally drives some of the
need for various platforms. Any new strategic enterprise application development
should not assume the persistence requirements to be relational. For example, data
that is transactional in nature could be stored in a relational store and twitter feed
could be stored in NoSQL structure.
This would mean bringing in complexity that introduces learning new interfaces but
a benefit worth the performance gain.
It requires that an enterprise has the important data engineering aspects in place to
handle enterprise data effectively. The following list covers a few critical data engin-
• Data architecture and design
• Database administration
Enterprise data can be classified into the following categories:
• Transactional data : It is the data generated to handle day-to-day affairs
within an enterprise and reveals a snapshot of ongoing business processing.
It is used to control and run fundamental business tasks. This category of
data usually refers to a subset of data that is more recent and relevant. This
data requires a strong backup strategy and data loss is likely to entail sig-
nificant monetary impact and legal issues. Transactional data is owned by
Enterprise Transactional systems that are the actual source for the data as
well. This data is characterized by dynamicity. For example, order entry, new
account creation, payments, and so on.
• Master and Reference data : Though we see Master data and Reference
data categorized under the same bucket, they are different in their own
sense. Reference data is all about the data that is usually outside the enter-
prise and is Standards compliant and usually static in nature. On the other
hand, Master data is similar in definition with the only difference that it origin-
ates from within the enterprise. Both Master and Reference data are referen-
ced by Transactional data and key to the operation of business. This data is