Look around at the technology we have today,
and it's easy to come to the conclusion that it's all about data. Organizations
are flooded with data. Not only that, but in an era of incredibly cheap storage
where everyone and everything are interconnected, the nature of the data we’re
collecting is also changing. For many businesses and software development
companies, their critical data used to be limited to their transactional databases and data warehouses.
As consumers, we have an increasing appetite
for rich media, both in terms of the movies we watch and the pictures and
videos we create and upload. We also, often without thinking, leave a trail of
data across the Web as we perform the actions of our daily lives.
Not only is the amount of data being generated
increasing, but the rate of increase is also accelerating. Not only software
development firms, but from emails to Facebook posts, from purchase histories
to web links, there are large data sets growing everywhere. The challenge is in
Database designing and extracting from
this data the most valuable aspects; sometimes this means particular data
elements, and at other times, the focus is instead on identifying trends and
relationships between pieces of data.
There's a subtle change occurring behind the
scenes that is all about using data in more and more meaningful ways. Large
companies have realized the value in data for some time and have been using it
to improve the services they provide to their customers, that is, us. Consider
how Google displays advertisements
relevant to our web surfing, or how Amazon
or Netflix recommend new products or
titles that often match well to our tastes and interests.
These corporations wouldn't invest in
large-scale data processing if it didn't provide a meaningful return on the
investment or a competitive advantage. There are several main aspects to big
data that should be appreciated in a remarkable manner.
Some questions only give value when asked of
sufficiently large data sets. Recommending a movie based on the preferences of
another person is, in the absence of other factors, unlikely to be very
accurate. Increase the number of people to a hundred and the chances increase
slightly. Use the viewing history of ten million other people and the chances
of detecting patterns that can be used to give relevant recommendations improve
Big data tools often enable the processing of
data on a larger scale and at a lower cost than previous solutions. As
a consequence, it is often possible to perform data processing tasks that were
previously prohibitively expensive.
In web development projects, the cost of
large-scale data processing isn't just about financial expense; latency is also a critical factor. A system
may be able to process as much data as is thrown at it, but if the average
processing time is measured in weeks, it is likely not useful. Big data tools
allow data volumes to be increased while keeping processing time under control, usually by matching the increased
data volume with additional hardware.