Edge Computing plays a vital role in Big Data systems, especially for data collection and processing, whether in real time or in batches Speed and data volume are critical factors in Big Data environments, and these can be optimised by processing at the edge of the network. Edge Computing enables data processing to happen locally, which reduces latency and is more efficient because it means that less data needs to be sent to centralised data centres. This is particularly beneficial for applications whose response time is crucial, such as the supervision of critical infrastructure, real-time event analysis and emergency response systems. Edge computing also makes it easier to carry out preliminary tasks, such as filtering and pre-processing the data before they are sent for more thorough analysis.

IoT (Internet of the Things) systems contain many devices that constantly generate enormous amounts of data, and Edge Computing is essential for managing this load and ensuring efficient processing. IoT devices can benefit greatly from edge computing, as it enables decisions to be made quickly and independently without relying on constant connectivity with the cloud. This is essential for applications such as autonomous vehicles, smart factories and energy grids, where delays in data processing can lead to significant losses or security issues Edge Computing allows data to be processed and analysed in the place where they are generated, improving the capacity for responding and reducing network congestion.

Real-time data collection is one of the main strengths of Edge Computing in the context of Big Data. Real-time data are critical for applications that require constant monitoring and immediate responses, such as traffic control in smart cities, energy management, health monitoring for patients and security in mass events. Edge computing makes it possible to react almost immediately to changing conditions, enabling responses to be rapid and efficient. Not only does this improve operating efficiency, it can also save lives in critical situations. The ability to process data in real time also reduces the amount of redundant information sent to the cloud, optimising the use of network resources.

Besides real-time processing, Edge Computing also improves data collection and batch processing, which is a common feature of many Big Data systems. Collected batch data can be processed locally at the edge before being uploaded to the cloud for long-term storage and further analysis This means that large volumes of data can be handled more efficiently, reducing the amount of data sent and stored centrally, reducing costs and improving the overall system performance. Pre-processing at the edge also means that the data can be cleaned up, normalised and prepared for more advanced analysis, ensuring that the data stored in the cloud is high-quality and ready for immediate use.

Edge Computing is necessary to optimise the capture and processing of data in Big Data environments, especially in the case of IoT applications. Doing the processing locally reduces latency and makes the network more efficient, making it possible to respond quickly and autonomously to events in real time. This is especially important in scenarios that require constant supervision and instant reactions. Batch processing at the edge also enables the efficient management of large volumes of information, reducing costs and improving the quality of the data stored and analysed in the cloud. Edge Computing and Big Data create a powerful synergy that boosts organisations’ capacity to gather, process and make use of data more effectively.