Big Data Computing Applications - Guide to Cloud Computing for Business and Technology Managers - page 443

TABLE 21.1
Scale of Data

Size of Data Scale of Data

1000 megabytes 1 gigabyte (GB)

1000 gigabytes 1 terabyte (TB)

1000 terabytes 1 petabyte (PB)

1000 petabytes 1 exabyte (EB)

1000 exabytes 1 zettabyte (ZB)

1000 zettabytes 1 yottabyte (YB)

If companies can analyze petabytes of data (equivalent to 20 million four
drawer file cabinets filled with text files or 13.3 years of HDTV content) with
acceptable performance to discern patterns and anomalies, businesses can
begin to make sense of data in new ways. Table 21.1 indicates the escalating
scale of data.
The list of features for handling data volume included the following:

• Nontraditional and unorthodox data processing techniques need to
be innovated for processing this data type.
• Metadata is essential for processing these data successfully.
• Metrics and KPIs are key to provide visualization.
• Raw data do not need to be stored online for access.
• Processed output needs to be integrated into an enterprise level ana-
lytical ecosystem to provide better insights and visibility into the
trends and outcomes of business exercises including CRM, optimi-
zation of inventory, and clickstream analysis.
• The enterprise data warehouse (EDW) is needed for analytics and
reporting.

21.1.1.2 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 customer 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 effectiveness, 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 behav-
iors. The velocity of data produced by user clicks on any website today is a
prime example for big data velocity.