Dataintensiva

Dataintensiva is a term used in information technology to describe systems, applications, or processes that rely heavily on data collection, storage, processing, and analysis. It emphasizes data as the dominant resource in the workload and often implies the need for scalable storage, fast data movement, and parallel processing. The term is commonly used to distinguish data-intensive workloads from compute-intensive ones.

Key characteristics include large data volumes (volume), high-velocity data streams (velocity), diverse data formats and sources (variety), and concerns about data quality and accuracy (veracity). Achieving acceptable performance typically requires distributed storage and compute, such as clustered file systems, data lakes or data warehouses, and parallel processing frameworks (for example Hadoop, Spark) as well as streaming platforms (Kafka, Flink).

Common domains include e-commerce personalized recommendations, real-time analytics on social networks, scientific simulations, genomics, climate modeling, Internet of Things, and large-scale log analysis. These workloads often employ data pipelines, ETL/ELT processes, and architectures that decouple storage from compute to scale cost-effectively (data lake, lakehouse, or warehouse patterns).

Key architectural patterns include lambda and kappa architectures, data-centric design, metadata management, data lineage, governance, and security. Challenges include ensuring data quality and governance, managing storage and compute costs, meeting latency requirements, and complying with privacy regulations.

Data-intensive computing is closely linked to the broader field of big data, data engineering, and cloud-native architectures. The term underscores the importance of scalable data infrastructure and governance in extracting timely insights from large and diverse data assets.