Edge computing data near its source rather than in centralized cloud data centers. This reduces latency dramatically, enabling real-time applications. IoT devices generate massive data that's impractical to send to the cloud. Edge computing performs initial processing locally, sending only relevant data upstream. Applications include industrial automation with predictive maintenance. Autonomous vehicles require millisecond decision-making that cloud latency can't provide. Smart cities use edge computing for traffic management and public safety. Healthcare benefits from real-time patient monitoring with edge processing. Content delivery networks (CDNs) are early examples of edge computing. 5G networks enable more sophisticated edge computing scenarios. AWS Wavelength and Azure Edge Zones provide cloud services at the edge. Challenges include device management, security, and data consistency. Edge devices often have limited compute and storage resources. Offline operation capabilities are crucial for many edge applications. Federated learning trains ML models across edge devices without centralizing data. Edge computing complements cloud computing rather than replacing it. The edge computing market is expected to reach $61.14 billion by 2028. Organizations should evaluate which processing belongs at the edge versus the cloud.