Pinakin Patel, Head of
Solutions Engineering
at MapR, explores the
growing trend towards
a new type of data
middleware – aka
‘dataware’ – that is
designed to do for data
what middleware did
for operating systems
and applications.
nformation Technology has
an overriding progression
centred on performance and
efficiency. The mainframes
of the 1970s moved to the racks of pizza
boxes in data centres of the 1990s that
are now the virtualised clusters within
clouds. On the application side, the highly
proprietary applications interfaces have
given way to more open APIs such as
SQL, RestAPI and S3.
I
One of the drivers is to remove
complexity and an early example is
the introduction of middleware as an
abstraction layer. The Google dictionary
description states of middleware:
‘Software that acts as a bridge between
an operating system or database and
applications, especially on a network.’
However, in the modern era, the stack of
hardware, middleware and applications
all connected by a network is much less
certain. With smartphone apps, the
cloud, shared networks, web-apps and a
whole host of hybrid systems, the role of
middleware as a glue to bind operating
systems to applications is still valid, but
middleware does not provide a consistent
conduit for the handling of digital data
which is the foundation for most business
use cases.
Data evolution
In the past, where most data was
generated from a big relational database,
the integration was simple. Today, we
have the data of a growing number of
formats and delivery types. In no order,
data can be structured and unstructured,
file, object, streaming real-time, inference
data and archived, plus several hybrid
types. The data might need a certain
type of encryption, higher availability
or accessibility to a third party for
governance issues. It might have
regulatory needs that mean it can only
be stored in a certain geography or for a
certain duration. The list of data centric
requirements along with a need for
scaleable performance is vast, complex
and growing.
To address this issue, there is a growing
trend towards a new type of data
middleware – termed ‘dataware’ –
that is designed to do for data what
middleware did for operating systems
and applications.
Dataware sits in the spectrum between
hardware and middleware, as a conceptual
layer to create the next level of abstraction
in the IT stack. Distinctly different from
databases or data warehousing, dataware
provides a platform-based approach
that handles all data in terms of ingest,
storage, availability, transformation and
The growing trend
towards ‘dataware’
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Issue 01
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