Many enterprises that collect large volumes of time-series data from storage, virtualization, and cloud environments often run into a known problem: retaining long-term insights (data) without overwhelming storage and compute. To solve this problem, time-series analytics platforms need to handle billions of records efficiently while still delivering actionable insights.

The solution we will discuss here is to build a dynamic data aggregation engine directly into the platform. This article looks at a vendor-agnostic approach for aggregating, transforming, and purging time-series data effectively. The goal is to make it easier to manage growth without sacrificing data quality or performance and reducing storage needed.