Edge Analytics vs. Cloud Analytics: Tradeoffs in Real-Time Data Processing

Amarnath Immadisetty

doi:10.70589/JRTCSE.2025.13.1.7

Authors

Amarnath Immadisetty Sr Manager, Software Engineering, Lowe's, United States of America Author

DOI:

https://doi.org/10.70589/JRTCSE.2025.13.1.7

Keywords:

Edge Analytics, Cloud Analytics, Real-Time Data Processing, IoT, Autonomous Systems, Latency, Bandwidth Dependency

Abstract

The increasing demand for real-time processing of data has accelerated rapid growth in edge analytics as well as cloud analytics. Edge analytics refers to performing analytics near the source, which is typically at a device level or local networks, whereas analytics on the cloud would need central cloud infrastructure for analyzing data. This article weighs both these two strategies against performance, scalability, cost, and complexity. Edge analytics operates on lower latency and reduced dependency on bandwidth. It is suitable for applications such as IoT and autonomous systems, where real-time output is critical. However, in some cases, the computational power of edge analytics will be limited, and scaling is difficult. In contrast, cloud analytics provides considerable processing capability and easier integration with large-scale data but may have higher latency and dependency on internet connectivity. This paper reviews these factors and provides insights on how to select the best solution for any given use cases and organizational needs; thus, helping businesses handle real-time data processing challenges.

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