Adatintenzív

Adatintenzív is a Hungarian term that translates to "data-intensive" in English. It describes systems, processes, or applications that are characterized by their need to process, store, and analyze very large volumes of data. The sheer scale of the data is a defining feature, often exceeding the capacity of traditional data processing methods.

Data-intensive applications are prevalent in many modern fields, including scientific research, big data analytics, machine learning, and the Internet of Things (IoT). These applications often require specialized hardware and software architectures designed for high throughput and scalability. Examples include large-scale simulations in physics or biology, real-time fraud detection systems, and recommendation engines that analyze user behavior across vast datasets.

Key challenges in developing and managing data-intensive systems include efficient data storage and retrieval, parallel processing capabilities, fault tolerance, and the ability to handle continuously growing data streams. The focus is on extracting meaningful insights and value from this massive influx of information, often in near real-time. The development of tools and frameworks like distributed file systems, distributed databases, and distributed computing platforms has been crucial in enabling the creation of such data-intensive solutions.