The edge computing revolution is on a similar path as the mobile computing revolution. From the big computers and servers located in remote rooms, which analyze the data and then provide information and solutions after processing it, to managing things on small devices at your fingertips, the scale is getting even smaller. Now is the time to know what edge computing is. And definitely, develop the skills needed to be part of companies that work with this technology. This article discusses why you should know about it, its benefits, drawbacks, and uses. Simultaneously, it also dives into the future of edge computing in the IoT (Internet of Things).

What is edge computing

Consider an autonomous car. It depends on receiving data from intelligent traffic control centers in real-time. The cars produce, analyze, and share the data with the remote servers. Then they analyze it among the upcoming data from other autonomous cars. Thus, these remote servers and clouds process the upcoming data, analyze it, and transmit the selective data back to the respective cars. The car has to process and act on this upcoming information. The latency and cross-connections with this communication can cause trouble. However, if you know what edge computing is, you might as well have the solution.

What is edge computing?

Simply put, it relocates a portion of the data computer resources from central rooms to locations closer to the source of the data.
Additionally, it provides storage capacity near the data sources. It also produces faster responses. Hence, edge computing also reduces the resources and clutter for data storage.

How does edge computing work?


IoT began with the simple act of delivering or receiving data.
However, what edge computing focuses on is a more current strategy. It enables sending, receiving, and analyzing data in conjunction with IoT applications.

Mainly, the networks work and provide their services at these three key points:

  • On-premises: where data centers store numerous racks of servers, have the resources to power and cool them, and have dedicated connectivity to outside services.
  • Colocation facilities: fully managed buildings that provide electricity, cooling, and connectivity as services host customer equipment.
  • Cloud service providers: partially visualize customer infrastructure and deliver services and applications on a per-use basis. Moreover, cloud service providers record operations as operational expenses rather than capital expenditures.

Edge computing architects would like to add a fourth category to this list. In this case, it uses the portability of smaller, containerized facilities with smaller and more modular servers. Which allows to reduce the distances between the processing point and the consumption point of functionality in the network.

Why is edge computing important?

People often ask, ‘What is edge computing and why is it important?’ We are all producers and consumers of data. The computing power and connecting devices accumulate a vast amount of data that causes a massive strain on the internet. Which also causes bandwidth and latency issues.
There is a rise in real-time applications with 5G fast wireless arrival and IoT devices. This requires minimal latency. Edge computing places storage, analytics, and processing close to the source of data creation.


The goal of edge computing is to move your applications closer to where data is generated and action is required. This allows for substantially faster response times (extremely little latency between when an event occurs and when a response occurs). You also benefit from less data traveling across the internet. Besides, this lowers back haul costs and keeps data more local, secure, and private. It also allows you to analyze more data at better resolutions and frequencies.


  • Minimal latency: There is no lag. Processors housed in small data centers closer to their intended use could open up new markets for computing services. Besides, cloud providers have yet to address this.
  • Autonomy: Local data processing minimizes the amount of data transferred, which requires substantially less bandwidth or connectivity time.
  • Data ownership: Edge computing enables raw data and processes it locally, concealing or shielding any sensitive data. Next, it transmits it to the cloud or primary data center, which may be located in another jurisdiction.
  • Cost-effective: Companies that first embraced the cloud for many of their apps may have discovered that bandwidth costs were greater than expected. Now they are seeking a less expensive alternative. Edge computing is a good fit.
  • AI: These algorithms demand vast amounts of processing power that run on cloud-based services. Furthermore, the development of AI chip-sets capable of doing jobs at the edge will result in the construction of new systems to handle those tasks.


  • Privacy and Security: When dissimilar, potentially less secure devices or cloud-based systems handle data at the edge, security issues arise.
  • Limitation of capability: Even a large-scale edge computing deployment serves a specified purpose at a predetermined size with limited resources and few services.
  • Connectivity: Although it bypasses traditional network restrictions, even the most tolerant device deployment will necessitate some amount of connectivity.
  • Data life-cycles: The majority of the data used in real-time analytics is short-term data. After doing an analysis, a company must pick which data to preserve and which to delete. Furthermore, it must protect the data that is in compliance with company and regulatory rules.
  • Physical maintenance: physical maintenance is important and requires upkeep. With recurrent battery and device replacements, IoT devices frequently have short lifespans. When gear fails, it requires maintenance and replacement. Maintenance must incorporate practical site logistics.

Use cases

  • Manufacturing: Edge computing offers real-time analytics and machine learning to discover production challenges and improve product quality. In addition, it aids in the use of environmental sensors across the production facility. Hence, they offer insights into the assembly and storage processes of each product part.
  • Workplace safety: Businesses can monitor working conditions or make sure that staff adhere to safety procedures by using the device to aggregate and analyze data. On-site cameras, employee safety devices, and many other sensors can be used to achieve this.
  • Better healthcare: Edge computing uses automation and machine learning to access data, filter out “normal” data, and spot problematic data so clinicians may respond quickly to prevent health crises in real time for patients.
  • Retail: It can assist with vendor ordering optimization, sales prediction, and the analysis of a variety of data to find commercial prospects.
  • Financial services: Customers can bank more quickly and securely using edge computing, and real-time analysis of ATM video feeds ensures even more security.


The sheer number of edge computing devices in the world is astonishing. IBM estimates that 15 billion edge devices are in the field today. Cisco Systems estimates that number will jump to 29.3 billion by next year. Juniper Research estimates that number will grow to 83 billion by 2024. The development of Micro Modular Data Centers (MMDCs) is a future alternative.
Nevertheless, the cost of hardware and the development of relevant software will define the future it has, at least in part.


Edge computing careers require the following skills:

  • Programming,Application development, and application architecture: Creating and deploying the applications requires these abilities.
  • Networking and connectivity: Understanding the operation of networks and connectivity is critical.
  • Cloud computing: Understanding how edge computing integrates into the greater IT landscape requires knowledge of cloud computing.
  • Security: Understanding security best practices is crucial since it creates unique security challenges.
  • Data analytics: Since this generates a large amount of data, understanding data analytics is essential for making sense of this data.

To build and reinforce these skills, Henry Harvin Education offers the best Internet of Things (IoT) course.

Pursuing a career in edge computing can be a smart choice for IT professionals trying to stay ahead of the technology curve. There are several job opportunities accessible in this rapidly expanding area for anyone with the right skills and expertise.


What is edge computing?

Processing power is pushed closer to the data source or the end user using edge computing.

What are edge computing’s primary use cases?

Edge computing has several applications, such as in manufacturing, safety at work, healthcare, retail, and finance.

What qualifications are required for an edge computing career?

Programming, application development and architecture, networking and connection, cloud computing, security, and data analytics are among the skills required for a career in edge computing.

Which businesses are involved in edge computing?

Numerous businesses, both large and small, are trying to compete in this market. A few businesses involved in edge computing are IBM, Microsoft, Google, and Amazon Web Services.

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