Database Reference
In-Depth Information
day, or other contexts, the volume of the data stream can vary dramatically.
For example, consider a stream of data carrying stock trades in an exchange.
Depending on trading activity, that stream can quickly swell from 10 to 100
times its normal volume. This implies that a Big Data platform not only has
to be able to support analytics of data in motion, but also has to scale effec-
tively to manage increasing volumes of data streams.
5. A Rich Library of Analytical Functions
and Tool Sets
One of the key goals of a Big Data platform should be to reduce the
analytic
cycle time
, the amount of time that it takes to discover and transform data,
develop and score models, and analyze and publish results. We noted earlier
that when your platform empowers you to run extremely fast analytics, you
have a foundation on which to support multiple analytic iterations and speed
up model development (the snowball gets bigger and rotates faster). Although
this is the desired end goal, there needs to be a focus on improving devel-
oper productivity. By making it easy to discover data, develop and deploy
models, visualize results, and integrate with front-end applications, your
organization can enable practitioners, such as analysts and data scientists, to
be more effective in their respective jobs. We refer to this concept as the
art of
consumability
. Let's be honest, most companies aren't like LinkedIn or Face-
book, with hundreds (if not thousands) of developers on hand, who are
skilled in new age technologies. Consumability is key to democratizing Big
Data across the enterprise. You shouldn't just
want
, you should
always demand
that your Big Data platform flatten the time-to-analysis curve with a rich set
of accelerators, libraries of analytic functions, and a tool set that accelerates
the development and visualization process.
Because analytics is an emerging discipline, it's not uncommon to find
data scientists who have their own preferred mechanisms for creating and
visualizing models. They might use packaged applications, use emerging
open source libraries, or adopt the “roll your own” approach and build the
models using procedural languages. Creating a restrictive development
environment curtails their productivity. A Big Data platform needs to support
interaction with the most commonly available analytic packages, with deep
integration that facilitates pushing computationally intensive activities from
