Database Reference
In-Depth Information
14.10.1. 2 Data Velocity
The business models adopted by Amazon, Facebook, Yahoo, and Google, which became the de
facto business models for most web-based companies, operate on the fact that by tracking cus-
tomer clicks and navigations on the website, you can deliver personalized browsing and shopping
experiences. In this process of clickstreams, there are millions of clicks gathered from users at
every second, amounting to large volumes of data. These data can be processed, segmented, and
modeled to study population behaviors based on time of day, geography, advertisement effective-
ness, click behavior, and guided navigation response. The result sets of these models can be stored
to create a better experience for the next set of clicks exhibiting similar behaviors. The velocity of
data produced by user clicks on any website today is a prime example for Big Data velocity.
The most popular way to share pictures, music, and data today is via mobile devices. The sheer
volume of data that is transmitted by mobile networks provides insights to the providers on the
performance of their network, amount of data processed at each tower, time of day, associated
geographies, user demographics, location, latencies, and much more. The velocity of data move-
ment is unpredictable and sometimes can cause a network to crash. The data movement and its
study have enabled mobile service providers to improve the quality of service (Qos), and associat-
ing these data with social media inputs has enabled insights into competitive intelligence.
The list of features for handling data velocity included the following:
Systems must be elastic for handling data velocity along with volume.
Systems must scale up and scale down as needed without increasing costs.
Systems must be able to process data across the infrastructure in the least processing time.
System throughput should remain stable independent of data velocity.
Systems should be able to process data on a distributed platform.
14.10.1. 3 Data Variety
Data come in multiple formats as these range from e-mails to tweets to social media and sensor
data. There is no control over the input data format or the structure of the data. The processing
complexity associated with a variety of formats is the availability of appropriate metadata for iden-
tifying what is contained in the actual data. This is critical when we process images, audio, video,
and large chunks of text. The absence of metadata or partial metadata means processing delays
from the ingestion of data to producing the final metrics and, more importantly, in integrating the
results with the data warehouse.
The list of features for handling data velocity included the following:
Scalability
Distributed processing capabilities
Image processing capabilities
Graph processing capabilities
Video and audio processing capabilities
14.10.2 Big Data Appliances
Big data analytics applications combine the means for developing and implementing algorithms
that must access, consume, and manage data. In essence, the framework relies on a technology
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