Database Reference
In-Depth Information
Velocity:
Data is collected from many new types of devices, from a growing number of
users and an increasing number of devices and applications per user. Data is also emitted
at a high rate from certain modern devices and gadgets. The design and implementation of
storage and processing must happen quickly and efficiently.
Figure
1-1
gives you a theoretical representation of Big Data, and it lists some possible components or types of
data that can be integrated together.
Figure 1-1.
Examples of Big Data and Big Data relationships
There is a striking difference in the ratio between
the speeds at which data is generated compared to the speed at which
it is consumed
in today's world, and it has always been like this. For example, today a standard international flight generates
around .5 terabytes of operational data. That is during a single flight! Big Data solutions were already implemented long
ago, back when Google/Yahoo/Bing search engines were developed, but these solutions were limited to large enterprises
because of the hardware cost of supporting such solutions. This is no longer an issue because hardware and storage costs
are dropping drastically like never before. New types of questions are being asked and data solutions are used to answer
these questions and drive businesses more successfully. These questions fall into the following categories:
•
Questions regarding social and Web analytics:
Examples of these types of questions include
the following: What is the sentiment toward our brand and products? How effective are our
advertisements and online campaigns? Which gender, age group, and other demographics are
we trying to reach? How can we optimize our message, broaden our customer base, or target
the correct audience?
•
Questions that require connecting to live data feeds:
Examples of this include the following:
a large shipping company that uses live weather feeds and traffic patterns to fine-tune its ship
and truck routes to improve delivery times and generate cost savings; retailers that analyze
sales, pricing, economic, demographic, and live weather data to tailor product selections at
particular stores and determine the timing of price markdowns.
