The Internet of Things (IoT) has radically changed multiple interactions with the world, generating vast amounts of data from connected devices. Their processing and storing, therefore, are key to deriving meaningful insights. Two primary architectures compete in this domain: edge computing and cloud computing. We’ll take a look right here at some of the salient differences between them and when to use each.
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Understanding Edge Computing and Cloud Computing
Cloud computing involves processing data. Such an approach is highly scalable, cost-efficient, and provides access to very powerful computing resources. However, for IoT applications with real-time requirements, or in terms of restricted network connectivity, cloud computing introduces latency.
Edge computing is a form of computing that conducts computation and data storage nearer to the location where it is needed, such as the source of the data. It is often at the edge of the network. The architecture is designed to excel in low-latency scenarios, with minimal bandwidth consumption and high data privacy.
When to Use Edge Computing?
Discover best use cases where you need to implement or deploy edge computing technology to get maximum benefit.
Low Latency Applications
Real-time applications, such as autonomous vehicles, industrial automation, and augmented reality, have great benefits from edge computing.
Low Connectivity to the Network
Edge computing serves to process the data that is generated in such regions where the reach of connectivity or networking infrastructures barely lay their networks.
Data Privacy
Edge computing brings the processing of sensitive data closer to the source, hence reducing the possibility of a data breach happening.
Network Consumption
By processing data locally, edge computing is able to cure network congestion.
When to Choose Cloud Computing?
Understand the advantages of cloud computing and when it’s the best fit for your IoT project.
Big Data Analytics
Cloud computing will provide the necessary underlying infrastructure to process large volumes of data generated by IoT.
Machine Learning
Training and deploying machine learning models require computational resources, which are often required at the level of the cloud.
Cost
For applications with lower latency requirements, cloud computing can be more cost-effective.
Scalability
Cloud platforms offer elastic scalability, allowing you to adjust resources based on needs.
Hybrid Approach: Best of Both Worlds
Many situations will be optimally served by the hybrid model using a combination of edge and cloud computing. In this case, the edge devices are responsible for the preliminary processing and filtering of data, while the cloud supports specialized analytics, machine learning, and storage of data. It provides flexibility to fulfill specific needs for different use cases in IoT applications.
