Big Data

Datasets too large, too fast-moving or too messy for ordinary tools to handle. The shorthand is volume, velocity, variety: more records than a database can sensibly hold, arriving faster than anyone can process them, in formats that do not line up. The term dates from the era when storage got cheap enough that companies started keeping everything without deciding what it was for, and it named the problem that created. Its answer was to stop moving data to the analysis and move the analysis to the data instead, spread across many machines at once. That shift is what made training on internet-scale text possible, so today’s models are downstream of it.