UNCOVERING THE LAYERS
additional data types and data processes
into a dataware layer.
AI and security
Currently the use case most relevant for
dataware is within the area of Artificial
Intelligence. For the concept to take off,
the current generation of pioneers are in
the process of creating and testing models
to teach machines how to spot events and
deal with different situations.
This process of Machine Learning is
largely dependent on access to lots of
data. Dataware is perfect for these types
of projects as it can handle almost any
type of data with the ability to scale
capacity almost infinitely by offloading
to the cloud as well as handling
requirements such as performance
tiering and backup.
The other major issue that dataware
can potentially help address is security.
The present situation where enterprises
keep multiple silos of data that typically
have separate access, encryption
60
Issue 01
and governance criteria is incredibly
difficult to secure. As data starts to
move from these individual silos into
other areas such as AI research, test and
development, the ability to apply controls
to ensure security and privacy are made
more complex.
In a dataware centric model, all data
flows through this data abstraction layer
which makes it potentially easier to
apply policy-based controls at a single
point instead of having to fragment the
security process.
Dataware is an evolving concept and
several pioneers in this space, including
MapR, are striving to ensure that it
supports the widest ecosystem of
open standards.
This is vital to ensure the technology
avoids the proprietary pitfalls of legacy
middleware platforms that tended to lock-
in customers rather than provide freedom
and choice. If these lessons can be learnt
and enacted, then it really will be time for
a new ware. ◊
DATAWARE IS
AN EVOLVING
CONCEPT
AND SEVERAL
PIONEERS IN
THIS SPACE,
INCLUDING
MAPR, ARE
STRIVING TO
ENSURE THAT
IT SUPPORTS
THE WIDEST
ECOSYSTEM
OF OPEN
STANDARDS.
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