How Can Pratiti Technologies Enable Edge Computing Solutions?

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As more IoT systems, smart facilities, and interconnected devices are launched, businesses are collecting ever-greater volumes of data closer to the point of operational action. While processing all of this data within a central cloud environment can introduce the challenges of latency, bandwidth, or poor connectivity, organizations are increasingly looking at edge computing services to process data nearer to its point of generation. We can help our clients create an edge-focused architecture to boost responsiveness and enhance operational intelligence by supporting real-time decision-making and developing more efficient technology environments. Edge computing: Bringing computation, analytics and data stores closer to the devices, machines, sensors, and users where the data is created.

Rather than sending all data to a distant data center before an application can take action, organizations run targeted workloads locally or nearby as close as possible to where the data originates. Industries in which even modest delays can cause real operational problems – including manufacturing, healthcare, automotive, retail, telecom, and smart infrastructure – can all be greatly served by a properly designed edge architecture. There is more to an edge-based strategy than simply installing servers at the network’s edge. Instead, organizations should carefully consider their particular workflows, their data flows, security requirements, current conditions of their connectivity and device infrastructure, and business objectives.

When evaluating an edge computing option, companies should consider how it can work with existing platforms, enterprise systems, the applications that will use the data, IoT devices that will source it, and security controls they already have in place.

At Pratiti Technologies, we bring a realistic approach that considers the whole technology picture we do not see edge computing as separate infrastructure. Reduce latency by running time-sensitive applications locally. By processing pertinent data locally or as close to its origin as possible, an application can respond to needs more quickly, making edge ideal for real-time applications.

An application handling equipment and sensor data from industrial machines, for example, can make local use of data to make recommendations more quickly. This can apply to smart building sensors and systems, retail applications or telematics. Filter data before sending it to the cloud.

By pre processing data locally, it’s possible to perform preliminary analysis and filtering, sending only relevant or summarized data to central cloud systems.

By filtering the data that you send upstream, it’s possible to reduce bandwidth requirements as well as storage needs in central data centers. Complement and extend the cloud with edge computing. The ideal edge computing solution may not be an entirely replacement for traditional cloud architectures, but an extension. Data-intensive processing can occur at the edge, with the cloud handling storage, central analytics, device management and reporting, and long-term archiving. With a well-integrated approach to hybrid architecture, organizations gain flexibility, scalability and maintain oversight of all connected devices and applications.

Leveraging the full scope of your technology environment, we can guide you in the implementation of successful edge computing solutions. Our practice brings expertise in software engineering, IoT cloud, data, and technology consulting to help organizations develop practical edge strategies, identify appropriate use cases, designing connected architectures, integrate systems with your existing technology environments, and develop apps which take advantage of edge sourced data, all the way addressing the real business challenges.