Edge computing is a distributed computing paradigm that brings data processing, storage, and application logic closer to the source of data generation—at the network edge—rather than relying on centralized cloud data centers. This architectural shift emerged to address the latency, bandwidth, and reliability challenges of cloud-first models, particularly as IoT devices, real-time AI inference, and 5G networks proliferate. By processing data locally, edge computing reduces round-trip time to distant servers, conserves bandwidth by filtering data at the source, and enables applications to function even when connectivity to the cloud is intermittent. In 2026, WebAssembly (Wasm), sovereign edge deployments, and agentic AI at the edge are reshaping how distributed applications are built and governed. The key insight: not all data needs to travel to the cloud—edge computing runs compute where the data lives, delivering millisecond-level responsiveness for applications where every millisecond counts.
What This Cheat Sheet Covers
This topic spans 17 focused tables and 141 indexed concepts, 128 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
A jump-to index of every table row in this cheat sheet.
An interactive map of every table and concept in this topic.
Table 1: Core Edge Computing Concepts
These foundational terms define the physical and logical building blocks of edge architecture. Understanding the distinction between edge nodes, gateways, and servers — and how they relate to data locality and cloud integration — is essential before engaging with any specific edge platform or use case.
| Concept | Example | Description | |
|---|---|---|---|
Industrial gateway, retail POS system | A physical device or server deployed at or near the data source that performs local computation and storage | ||
Regional micro data center, cell tower compute | A more capable computing resource positioned at the network edge, often serving multiple edge nodes or end devices | ||
IoT gateway aggregating sensors | A device that collects data from multiple endpoints, performs preprocessing, and routes filtered data to cloud or edge servers | ||
Camera analyzing video on-device | Data analyzed and acted upon at the point of creation without sending raw data to centralized servers | ||
CDN PoP in major city, 5G base station | A geographic site where edge infrastructure is deployed to reduce latency for users in that region | ||
Patient records processed in hospital | Keeping data geographically close to its source to reduce latency, meet compliance requirements, and improve privacy | ||
Local inference at edge, training in cloud | A hybrid model where time-critical processing happens at the edge while heavy computation and long-term storage reside in the cloud | ||
Workload split across factory floor devices | Computation divided across multiple edge nodes rather than centralized in one location, improving fault tolerance and scalability |