As technologies advance at a rapid pace, businesses across industries are looking for innovative ways to process massive amounts of data, remain agile in their operations, and provide seamless digital experiences to their customers.
Edge computing refers to processing data close to the source from which it is generated, such as at local network edges, enterprise locations, or even individual user devices. Cloud computing involves storing, managing, and processing data via remote servers hosted on the internet. While these may seem like competing technologies, many industries have found success in combining the strengths of edge and cloud computing.
In this blog, we will explore the top nine industries that are uniquely positioned to utilize both edge and cloud working in tandem.
1. Retail: Real-Time Analytics at the Edge, Scalable Infrastructure in the Cloud
The retail industry relies on analyzing massive amounts of customer behavior data in real-time to power personalized recommendations, manage inventory levels, detect fraud, and more. Edge devices placed throughout physical stores and warehouses allow the collection of data with minimal latency.
This data is then sent to cloud server for advanced analytics at scale, as well as long-term storage. The combination of low-latency edge processing and elastic cloud infrastructure enables retailers to gain valuable insights from customer interactions the moment they occur while avoiding costly on-premise servers that cannot scale with business needs.
2. Manufacturing: Monitor Equipment Performance with Edge and Analyze Trends in the Cloud
Factory floors generate terabytes of IoT sensor data daily from equipment, production lines, environmental controls, and other operational systems. Processing this deluge of data at the edge of on-premise gateways provides real-time equipment performance monitoring and predictive maintenance.
The same data is replicated on cloud servers, where it can be aggregated with historical data for long-term storage, advanced diagnostics of production trends, and machine learning on entire fleets of equipment across different factory locations. Edge computing ensures manufacturing operations continue uninterrupted, while the cloud facilitates improvements based on big-picture insights.
3. Transportation: Low-Latency Analytics for Vehicles, Cloud Infrastructure for Back-End Services
Vehicles of all types—from trains and aircraft to trucks and passenger cars—increasingly rely on data connectivity. Edge devices in moving vehicles allow for latency-sensitive functions like computer vision, navigation, and autonomous driving capabilities.
Transportation data is also transmitted to cloud servers for applications like predictive maintenance, fleet management, infotainment services, over-the-air updates, and more. This hybrid edge-cloud model enables low-latency operations critical for on-board safety systems while leveraging scalable, centralized cloud services for enhanced back-end experiences.
4. Healthcare: Real-Time Patient Monitoring at Hospitals, Longitudinal Health Records in the Cloud
Healthcare facilities generate massive amounts of patient-generated data from bedside devices, digital records, and IoT sensors. Edge servers placed locally at hospitals allow caregivers to continuously monitor high-risk patients and receive alerts about urgent conditions with no latency.
The same data seamlessly feeds into centralized electronic health record systems hosted in the cloud. This provides longitudinal views of patient histories across different providers for improved care coordination and research insights. The edge-cloud combination optimizes data availability, whether for real-time triage or long-term health analytics.
5. Oil & Gas: Remote Monitoring of Energy Assets, Analytics on Vast Historical Data
With oil rigs, pipelines and energy infrastructure spread across vast geographical areas, the energy sector relies on IoT sensors to remotely monitor assets in often harsh and hazardous conditions.
Edge gateways at these remote sites collect sensor data with low latency. The data is then sent over satellite networks to cloud server platforms for storage, advanced analytics, predictive maintenance, and safety applications using machine learning. This hybrid model keeps energy operations running smoothly through real-time edge insights while leveraging vast historical data archives in the cloud.
6. Smart Cities: Low-Latency Analytics for Surveillance Cameras, Aggregated Insights for Planners
Smart city initiatives involve instrumenting public infrastructure with a plethora of IoT sensors for applications like traffic management, emergency response and more. Video surveillance cameras leverage edge servers for immediate detection of incidents or anomalies.
The same sensor data streams to cloud platforms, where it can be aggregated from multiple locations to identify city-wide trends, optimize resource allocation. Seamlessly, edge and cloud work together to enable both real-time responsiveness and holistic, strategic decision-making for municipalities.
7. Gaming: Render Graphics at Edge, Leverage Cloud for Backend Services
The gaming industry demands low-latency, graphics-intensive experiences. Indeed, Edge servers located near gamers allow hardware-accelerated rendering and processing of immersive 3D graphics without lag.
Gameplay data, achievements, profiles and multiplayer services are concurrently managed on cloud backends for global scaling. However, this hybrid model optimizes the in-game experience with edge computing, while cloud infrastructure supports engaging online ecosystems around games.
8. Media & Entertainment: Low-Latency Streaming, Cloud for Analytics and Monetization
Edge nodes placed within Internet Service Provider networks enable streaming ultra-high-definition video and audio to viewers with minimal delay. Concurrently, the cloud facilitates big data analytics on viewership and engagement patterns, A/B testing to optimize monetization, digital rights management, and personalization at scale. Also, edge handles bandwidth-intensive content delivery, while the cloud server powers sophisticated backend capabilities for an enhanced entertainment experience.
9. Financial Services: Real-Time Trading Systems at Edge, Risk Management and Fraud Detection in Cloud
Millisecond responses are mission-critical for financial trading systems. Therefore, edge colocation servers located within exchange data centers allow ultra-low-latency market data feeds and order matching.
Meanwhile, the cloud hosts risk management platforms, anti-money laundering solutions, and other compliance applications that require massive historical data processing. This hybrid approach keeps trading operations seamlessly running at wire-speed. Also, edge proximity while leveraging elastic cloud resources for data-hungry oversight and regulatory functions.
Key Summary
As these use cases demonstrate, edge computing optimizes latency and reliability for mission-critical applications through proximity. Cloud servers provide scalable infrastructure for data-intensive workloads, long-term storage, advanced analytics, and services requiring a global scope. Together, edge and cloud form a powerful combination that allows industries across derive value from data in new ways. Moreover, Optimizing real-time operations or gaining strategic insights. Considering how edge and cloud can work in tandem is a wise approach for any data-driven business.