METANOWMore than a large spreadsheet
Big data describes data whose scale, arrival rate or diversity challenges conventional processing approaches. It can include structured records, documents, sensor streams, images and operational logs.
From collection to use
A pipeline collects data, validates it, stores it and makes it available for processing and analysis. Some tasks run in batches; others process events as they arrive. The architecture should match the actual business question and response time.
Quality matters as much as quantity
Duplicate records, missing values, inconsistent timestamps and unclear identifiers can distort results. Document where data came from, what it means, who can use it and how long it should be kept.
An operational example
Equipment readings, maintenance records and production events may be combined to investigate downtime. Before predicting failures, establish whether the records describe the same assets and whether past events were labelled consistently. More data does not automatically mean better decisions.
Sources & further reading
Reviewed 19 October 2026. This is a technology learning session; examples describe possible designs, not a claim that a particular integration is active.