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Edge computing is fundamentally transforming traditional cloud-based data frameworks. By enabling data processing and analysis closer to the source, edge paradigms allow for faster and more efficient architectures while expanding whats possible.

In this article, Ill cover:

Up until recently, most data pipelines relied on a centralized cloud model. Data from endpoints like mobile devices, autonomous vehicles and IoT sensors flowed upwards into cloud data centers for processing, analysis and storage before sending back results.

This model introduced latency since data had to traverse wide area networks to move back and forth from the cloud. It also led to exorbitant costs when huge numbers of devices simultaneously communicated with cloud servers.

Maintaining constant connectivity to the cloud from swarms of distributed endpoints proved challenging. Real-time responsiveness was difficult when it took data multiple seconds to do the round trip from endpoint to cloud and back again.

For innovations on the edge like self-driving cars and industrial automation, milliseconds matter. Complex analytics on massive datasets also pushed cloud infrastructure to its limits in both compute performance and expenditure.

Finally, consolidating sensitive data like medical records or proprietary telemetry data in centralized clouds also raised privacy and security issues.

Edge computing solutions have emerged to sidestep the limitations of cloud-centric models. Instead of routing all data and compute operations through centralized servers, edge

More here:
How Edge Computing Is Transforming Data Processing and Cloud Architectures - Medium