Big data

Big data refers to datasets whose volume, velocity, or variety exceeds the practical processing capacity of conventional relational database systems, requiring distributed computing architectures and specialized tooling to store, process, and analyze. The classic framing uses the "three Vs": volume (terabytes to petabytes of records), velocity (data arriving continuously or in near-real-time streams), and variety (structured tables, semi-structured logs, unstructured text, images, clickstreams, and more). Technologies such as Apache Hadoop, Apache Spark, and cloud-native data lakes were built specifically to address these constraints at commodity infrastructure costs.

In commerce, big data manifests in the form of customer clickstreams, transaction logs, product catalog metadata, social signals, supply chain sensor data, and third-party enrichment feeds — all accumulating simultaneously at high speed. The business value is not in the raw volume itself but in the analytical patterns that only become visible at scale: subtle demand signals, micro-segments of buyer behavior, or early indicators of supply disruption. Organizations that can process and act on this data faster than competitors gain meaningful advantages in pricing, inventory positioning, and customer experience, which is why big data infrastructure is treated as a strategic asset rather than a pure cost center.