In addition, NoSQL key-value stores have received strong adoption because storage tools
that back them for big data storage are not dependent because of relational schema or those
consistency requirements that plague RDBMS. In autonomous IoT applications, it is possible to
decentralize storage while data remains with those projects that produce it after which data is
not forwarded to the system. The constraints of these objects have restricted the storage capa-
bility, particularly if you compare it with the centralized storage model.
Processing/Analysis
This stage is concerned with the continuous analysis and retrieval operations along with the
archived and stored data that can help to provide useful insights from the past data and help
with future predictions to identify patterns and trends. Similarly, it can also help to discover
data irregularities which require additional action or inspection. When permanent data storage
is not required by an autonomous IoT subsystem and instead the storage and processing are kept
in the network, in that case, you can benefit from in-network processing to address localized
or real-time queries.
ANALYTICS
Data and IoT is interlinked. The consumption and production of data continue to grow quickly.
This influx has a direct impact on IoT adoption. By 2020, it is expected that there will be more
than 30 billion IoT devices.
When IoT devices produce data, it is valuable only when it is processed through anal-
ysis. Therefore, data analytics is an important part of the puzzle. Analytics is a set of pro-
cedures that is executed to inspect small and large datasets having distinct characters in
order to get actionable insights and derive out meaning from the conclusions. Such output
is commonly generated through statistics, patterns, and trends, allowing companies to take
necessary decisions.
What role analytics play in IoT? Is it really helpful?
Flash Question
Data analytics play an important role in the expansion of the Internet of Things. With the
help of analytics tools, companies can extract meaning out of their datasets with the following
benefits.
Structure
IoT systems deal with dierent types of data like structured, unstructured, or semi-structured
datasets. Similarly, their formats can vary too. By using automated analytics tools, managers can
analyze each of these data types and formats.
Volume
IoT applications work with large datasets in massive clusters. Companies have to handle these
voluminous amounts of data and process them to extract the required patterns. Analytics tools
can easily analyze these data sets in conjunction with real-time data.
Chapter 5 IoT Core Modules 129
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