"Edge Computing in Retail:
Personalizing the Customer
Experience"
Report Overview
The edge computing market size is expected to be worth around USD 206 billion by 2032
from USD 40 billion in 2022, growing at a CAGR of 18.3% during the forecast period from
2023 to 2032.
How does edge computing differ from cloud
computing?
Cloud computing and edge computing are two distinct paradigms
within the realm of computing. Cloud computing is about the storage
and processing of large quantities of data that are not structured at
the same at a time, the edge computer is focused on processing data
in real time as well as communication among devices .
In cloud computing, information is processed at a central area, for
example the data center, and can be accessed from a remote location
via the internet . However, edge computing can be described as a
distributed computing model which brings computing as well as data
storage closer to the origin of the data. Processing of data occurs in
the edges of networks near the device which generated the data as
opposed to the central place .
The primary distinction between cloud computing and edge computing
lies in the place the data processing happens. In the case of edge
computing, processing occurs where data is created, whereas cloud
computing is where data processing takes place within an unreachable
central reserves of data .
A further difference between the two models is the way they are
applied. Edge computing is preferred to cloud computing when in
remote places that have only a limited connection to a central place .
Edge computing enables real-time decision-making through processing
data at the edge while also speeding data transfers into and out of the
cloud. IoT devices, vehicles that are automated and augmented
reality/virtual-reality (AR/VR) applications have low latency
requirements especially through edge computing .
2
Which is better for IoT devices, cloud or edge computing?
Cloud computing and edge computing are two distinct paradigms
within the realm of computing. While cloud computing involves the
storage and processing massive quantities of data that is unstructured
simultaneously edge computing concentrates on processing data in
real time and the communication among devices .
For IoT gadgets, the decision between cloud and edge computing is
based on the particular application. Edge computing is more preferred
to cloud computing for remote areas in which there is a the possibility
of having no or limited connectivity to a centrally located area . Edge
computing enables real-time decision-making through processing data
at the edge while also speeding the transfer of data into and out of the
cloud. IoT devices, autonomous vehicles and augmented
reality/virtual-reality (AR/VR) applications have low latency
requirements especially through edge computing .
Contrarily cloud computing is ideal for apps that require massive
processing and storage capacity for big data, such as large-scale
analysis of data, machine learning as well as artificial intelligence .
Cloud computing allows companies to launch their apps quickly and
with a minimal price barrier for the entry point. Additionally, it offers a
an array of pricing options, unlimited computation on demand,
simplified IT administration, quick upgrades, and reliability .
In conclusion, cloud and edge computing come with their own benefits
and uses They differ on the location the location where processing of
data takes place and the applications they serve. Edge computing is
more suitable to IoT devices that are located in remote areas that
3
have only a limited connection to a central place. Cloud computing is
better to applications that need large-scale storage and processing.
Challenges:
1. Security concerns: The most significant problems in edge
computing is the need to guarantee secure data. Since data
processing takes place on the edge is crucial to secure it from
breaches and threats.
2. Management Complexity The management of a distributed
edge device network can be difficult. Making sure that updates
and operations are seamless over these devices can be difficult.
3. Scalability With the increasing increase in the number of
devices with edge capabilities increasing the capacity of the
infrastructure needed to handle this increase can be a challenge.
Opportunities:
1. Real-Time Information: Edge computing is able to provide
real-time data analysis and opens doors to industries like
healthcare to provide instant monitoring of patients and
treatment.
2. Low Latency applications that require very low latency like
online gaming and autonomous vehicles are greatly benefited by
edge computing.
3. Energies Efficiency Edge computing is a way to cut down on
the energy use associated in data transmission to central data
centers, which makes it more eco-friendly.
4
Market Segmentation
The market for edge computing can be divided into several kinds of
categories:
1. For Industries: Edge computing solutions can be tailored to
certain industries like manufacturing, healthcare and logistics,
retail and telecommunications.
2. by Deployment Model Edge computing is deployed in various
ways such as on premises or at the edge of the network as well
as in cloud.
3. by Services The services that are related to the use of edge
computing comprise Edge analytics and security products and
devices for edge computing such as gateways and sensors.
5