The History of OLAP: A ’90s Solution with Limitations
In the 1990s, OLAP emerged as a powerful tool for making sense of data sets from burgeoning back-office applications. Technically, it relied heavily on precomputing everything before queries middle east rcs data were executed. – aggregations of data designed to provide instant answers to pre-defined queries. While effective for its time, this approach came with significant drawbacks.
One major issue was data freshness. The OLAP cubes were typically built on a weekly schedule, often over a weekend. If new data arrived on Monday, Tuesday, or any other day after the cube was built, that data wouldn’t be reflected in analyses until the next build. This delay rendered the insights increasingly irrelevant in fast-moving industries.
Another drawback was the cost of storage. To support both granular queries and aggregated insights, OLAP systems needed to store detailed raw data alongside fully aggregated cubes. The resulting storage overhead was prohibitive, particularly as datasets grew in complexity and size.