The common thread between old and new OLAP lies in figuring out how to scan fewer rows of data to deliver answers quickly. The older approach was relatively crude, often reducing millions of rows to thousands by discarding key pieces of cardinality. For instance, a dataset containing daily orders might be aggregated into monthly orders, cutting the volume of data by roughly 30 times (30 days in a month). However, this approach had a significant drawback: once aggregated, the daily-level data was lost, making it impossible to go back.
Modern OLAP, exemplified by innovations like partial middle east rcs data caching, offers a more flexible solution. Instead of fully aggregating data and discarding granularity, partial caching computes and stores intermediate aggregations – for example, weekly summaries. This approach enables querying at multiple levels of granularity – daily, weekly, or monthly – from the same dataset. When querying for monthly data, instead of scanning 30 daily records, only four weekly records need to be processed. This partial aggregation strikes a balance, preserving flexibility and efficiency without sacrificing data detail.
Adoption at Breakneck Speed
The resurgence of OLAP is evident in its rapid adoption across industries. Companies like Uber, Stripe, and LinkedIn leverage modern OLAP systems to power real-time dashboards, personalized recommendations, and operational analytics. These organizations rely on Apache Pinot, a real-time distributed OLAP data store designed for ultra-fast query processing on large-scale datasets to handle millions of queries per second, ensuring insights remain actionable and up to date.
From its static beginnings in the ’90s to its dynamic, big-data-driven rebirth today, OLAP has evolved into a must-have tool for any data-driven organization.